{"id":10947,"date":"2024-04-09T07:29:37","date_gmt":"2024-04-09T11:29:37","guid":{"rendered":"https:\/\/blogs.mathworks.com\/student-lounge\/?p=10947"},"modified":"2024-04-11T22:25:41","modified_gmt":"2024-04-12T02:25:41","slug":"predicting-time-to-diagnosis-for-the-wids-datathon-2","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/student-lounge\/2024\/04\/09\/predicting-time-to-diagnosis-for-the-wids-datathon-2\/","title":{"rendered":"Predicting Time to Diagnosis for the WiDS Datathon #2"},"content":{"rendered":"
\n
In today\u2019s blog, Grace Woolson <\/a>will show how you can use MATLAB and machine learning to make meaningful deductions from healthcare data for patients who have been diagnosed with metastatic breast cancer. Over to you Grace!<\/em><\/div>\n

Introduction<\/h2>\n
In this blog, I will show how you can use MATLAB for the WiDS Datathon 2024<\/a> using the dataset for the WiDS Datathon #2, which runs from April 9th 2024 – June 1st 2024. This challenge tasks participants with creating a model that can predict how long it takes for a patient with metastatic breast cancer to receive a diagnosis based on patient and geographic data. This can help identify relationships between demographics or environmental conditions with the likelihood of getting timely treatment. Please note that this tutorial is based on a subset of the data and there may be slight differences between this dataset and the one you download from Kaggle.<\/div>\n
MathWorks is happy to support participants of the Women in Data Science Datathon 2024 by providing complimentary MATLAB licenses, tutorials, workshops, and additional resources. To request complimentary licenses for you and your teammates, go to this <\/span>MathWorks site<\/span><\/a>, click the \u201cRequest Software\u201d button, and fill out the software request form.<\/span><\/div>\n
This tutorial will walk through the following steps of the model-making process:<\/div>\n
    \n
  1. Importing a Tabular Dataset<\/a><\/li>\n
  2. Preprocessing the Data<\/a><\/li>\n
  3. Exploring Tabular Data<\/a><\/li>\n
  4. Choosing and Creating Features<\/a><\/li>\n
  5. Training a Machine Learning Model<\/a><\/li>\n
  6. Making New Predictions and Exporting Submissions<\/a><\/li>\n<\/ol>\n

    Import Data<\/h2>\n
    First, make sure the ‘Current Folder’ is the folder where you saved the data. If you have not already done so, you can download the data from Kaggle after you register<\/a> for the datathon. The data is provided as a .CSV file, so you can use the readtable<\/span><\/a> function to import the whole file as a table.<\/div>\n
    \n
    \n
    dataFolder = fullfile(pwd);<\/span><\/div>\n<\/div>\n
    \n
    trainDataFilename = ‘training.csv’<\/span>; <\/span><\/div>\n<\/div>\n
    \n
    allTrainData = readtable(fullfile(dataFolder, trainDataFilename))<\/span><\/div>\n
    \n
    \n
    \n
    <\/div>\n
    <\/div>\n<\/div>\n<\/div>\n
    \n
    \n
    allTrainData = 13173\u00d7152 table <\/span><\/div>\n
    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    <\/th>\npatient_id<\/th>\npatient_race<\/th>\npayer_type<\/th>\npatient_state<\/th>\npatient_zip3<\/th>\nRegion<\/th>\nDivision<\/th>\npatient_age<\/th>\npatient_gender<\/th>\nbmi<\/th>\nbreast_cancer_diagnosis_code<\/th>\nbreast_cancer_diagnosis_desc<\/th>\nmetastatic_cancer_diagnosis_code<\/th>\nmetastatic_first_novel_treatment<\/th>\nmetastatic_first_novel_treatment_type<\/th>\npopulation<\/th>\ndensity<\/th>\nage_median<\/th>\nage_under_10<\/th>\nage_10_to_19<\/th>\nage_20s<\/th>\nage_30s<\/th>\nage_40s<\/th>\nage_50s<\/th>\nage_60s<\/th>\nage_70s<\/th>\nage_over_80<\/th>\nmale<\/th>\nfemale<\/th>\nmarried<\/th>\n<\/tr>\n<\/thead>\n
    1<\/th>\n268700<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘AR’<\/td>\n724<\/td>\n‘South’<\/td>\n‘West\u00a0South\u00a0Central’<\/td>\n39<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘C50912’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n3.9249e+03<\/td>\n82.6283<\/td>\n42.5750<\/td>\n11.6050<\/td>\n13.0317<\/td>\n10.8667<\/td>\n11.8017<\/td>\n12.2917<\/td>\n13.2167<\/td>\n13.4717<\/td>\n10.0717<\/td>\n3.6350<\/td>\n51.4317<\/td>\n48.5683<\/td>\n51.0483<\/td>\n<\/tr>\n
    2<\/th>\n484983<\/td>\n‘White’<\/td>\n”<\/td>\n‘IL’<\/td>\n629<\/td>\n‘Midwest’<\/td>\n‘East\u00a0North\u00a0Central’<\/td>\n55<\/td>\n‘F’<\/td>\n35.3600<\/td>\n‘C50412’<\/td>\n‘Malig\u00a0neoplasm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n2.7454e+03<\/td>\n51.7936<\/td>\n43.5351<\/td>\n11.2247<\/td>\n12.1922<\/td>\n11.4467<\/td>\n11.0065<\/td>\n11.3545<\/td>\n14.3922<\/td>\n14.1507<\/td>\n9.1727<\/td>\n5.0506<\/td>\n49.3234<\/td>\n50.6766<\/td>\n49.4753<\/td>\n<\/tr>\n
    3<\/th>\n277055<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘CA’<\/td>\n925<\/td>\n‘West’<\/td>\n‘Pacific’<\/td>\n59<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘1749’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n3.8343e+04<\/td>\n700.3375<\/td>\n36.2795<\/td>\n13.2667<\/td>\n15.6641<\/td>\n13.4949<\/td>\n13.4538<\/td>\n12,4000<\/td>\n11.5846<\/td>\n10.4667<\/td>\n6.3769<\/td>\n3.2846<\/td>\n49.9897<\/td>\n50.0103<\/td>\n48.8077<\/td>\n<\/tr>\n
    4<\/th>\n320055<\/td>\n‘Hispanic’<\/td>\n‘MEDICAID’<\/td>\n‘CA’<\/td>\n900<\/td>\n‘West’<\/td>\n‘Pacific’<\/td>\n59<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘C50911’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0right\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n3.6054e+04<\/td>\n5.2943e+03<\/td>\n36.6538<\/td>\n9.7615<\/td>\n11.2677<\/td>\n17.2339<\/td>\n17.4415<\/td>\n13.0908<\/td>\n12.3046<\/td>\n9.4077<\/td>\n5.6738<\/td>\n3.8246<\/td>\n50.5108<\/td>\n49.4892<\/td>\n33.4785<\/td>\n<\/tr>\n
    5<\/th>\n190386<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘CA’<\/td>\n934<\/td>\n‘West’<\/td>\n‘Pacific’<\/td>\n71<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘1748’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0other\u00a0specified\u00a0sites\u00a0of\u00a0female\u00a0breast’<\/td>\n‘C7951’<\/td>\nNaN<\/td>\nNaN<\/td>\n1.3700e+04<\/td>\n400.4763<\/td>\n41.7816<\/td>\n10.0316<\/td>\n16.4342<\/td>\n12.9710<\/td>\n11.2921<\/td>\n10.0868<\/td>\n11.5605<\/td>\n13.2790<\/td>\n8.7842<\/td>\n5.5316<\/td>\n51.9895<\/td>\n48.0132<\/td>\n48.2079<\/td>\n<\/tr>\n
    6<\/th>\n559027<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘IN’<\/td>\n461<\/td>\n‘Midwest’<\/td>\n‘East\u00a0North\u00a0Central’<\/td>\n63<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘1749’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified’<\/td>\n‘C786’<\/td>\nNaN<\/td>\nNaN<\/td>\n9.3229e+03<\/td>\n274.7371<\/td>\n40.1237<\/td>\n12.2300<\/td>\n13.8800<\/td>\n11.5317<\/td>\n11.9350<\/td>\n12.5517<\/td>\n13.9117<\/td>\n13.0467<\/td>\n7<\/td>\n3.9067<\/td>\n50.9817<\/td>\n49.0183<\/td>\n57.1617<\/td>\n<\/tr>\n
    7<\/th>\n293747<\/td>\n‘White’<\/td>\n‘MEDICARE\u00a0ADVANTAGE’<\/td>\n‘OH’<\/td>\n448<\/td>\n‘Midwest’<\/td>\n‘East\u00a0North\u00a0Central’<\/td>\n57<\/td>\n‘F’<\/td>\n33.1000<\/td>\n‘C50412’<\/td>\n‘Malig\u00a0neoplasm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast’<\/td>\n‘C799’<\/td>\nNaN<\/td>\nNaN<\/td>\n5.8906e+03<\/td>\n122.3929<\/td>\n42.4536<\/td>\n12.4286<\/td>\n13.1893<\/td>\n10.8089<\/td>\n10.7321<\/td>\n13.0411<\/td>\n13.2786<\/td>\n14.2804<\/td>\n7.5732<\/td>\n4.6786<\/td>\n49.9107<\/td>\n50.0893<\/td>\n55.8696<\/td>\n<\/tr>\n
    8<\/th>\n517596<\/td>\n‘White’<\/td>\n‘COMMERCIAL’<\/td>\n‘DE’<\/td>\n198<\/td>\n‘South’<\/td>\n‘South\u00a0Atlantic’<\/td>\n56<\/td>\n‘F’<\/td>\n31.0500<\/td>\n‘C50411’<\/td>\n‘Malig\u00a0neoplm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0right\u00a0female\u00a0breast’<\/td>\n‘C792’<\/td>\nNaN<\/td>\nNaN<\/td>\n2.2036e+04<\/td>\n1.4505e+03<\/td>\n41.6300<\/td>\n11.0300<\/td>\n11.9800<\/td>\n12.1100<\/td>\n13.6900<\/td>\n11.6600<\/td>\n13.9500<\/td>\n12.9700<\/td>\n7.6900<\/td>\n4.9700<\/td>\n47.8200<\/td>\n52.1800<\/td>\n42.0300<\/td>\n<\/tr>\n
    9<\/th>\n533188<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘LA’<\/td>\n706<\/td>\n‘South’<\/td>\n‘West\u00a0South\u00a0Central’<\/td>\n65<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘C50212’<\/td>\n‘Malig\u00a0neoplasm\u00a0of\u00a0upper-inner\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n7.2198e+03<\/td>\n531.0590<\/td>\n39.5421<\/td>\n12.4474<\/td>\n14.7868<\/td>\n11.0026<\/td>\n12.5368<\/td>\n11.6868<\/td>\n14.6947<\/td>\n12.4789<\/td>\n5.8816<\/td>\n4.4947<\/td>\n50.5500<\/td>\n49.4500<\/td>\n49.0737<\/td>\n<\/tr>\n
    10<\/th>\n639484<\/td>\n‘White’<\/td>\n‘COMMERCIAL’<\/td>\n‘CA’<\/td>\n922<\/td>\n‘West’<\/td>\n‘Pacific’<\/td>\n60<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘C50912’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n1.6550e+04<\/td>\n245.0979<\/td>\n44.2326<\/td>\n9.8872<\/td>\n10.4149<\/td>\n13.6723<\/td>\n11.3894<\/td>\n9.1447<\/td>\n15.5638<\/td>\n14.6277<\/td>\n10.1106<\/td>\n5.2298<\/td>\n54.2000<\/td>\n45.8000<\/td>\n46.5192<\/td>\n<\/tr>\n
    11<\/th>\n366431<\/td>\n‘Black’<\/td>\n‘MEDICARE\u00a0ADVANTAGE’<\/td>\n‘PA’<\/td>\n191<\/td>\n‘Northeast’<\/td>\n‘Middle\u00a0Atlantic’<\/td>\n71<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘C50911’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0right\u00a0female\u00a0breast’<\/td>\n‘C7989’<\/td>\nNaN<\/td>\nNaN<\/td>\n3.1948e+04<\/td>\n5.5122e+03<\/td>\n35.7191<\/td>\n10.8532<\/td>\n10.9511<\/td>\n18.1596<\/td>\n17.3489<\/td>\n11.6468<\/td>\n11.0979<\/td>\n10.6425<\/td>\n5.9426<\/td>\n3.3511<\/td>\n48.3085<\/td>\n51.6915<\/td>\n32.4915<\/td>\n<\/tr>\n
    12<\/th>\n793091<\/td>\n‘White’<\/td>\n‘MEDICARE\u00a0ADVANTAGE’<\/td>\n‘OH’<\/td>\n453<\/td>\n‘Midwest’<\/td>\n‘East\u00a0North\u00a0Central’<\/td>\n73<\/td>\n‘F’<\/td>\n23.6100<\/td>\n‘C50811’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0ovrlp\u00a0sites\u00a0of\u00a0right\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n6.4682e+03<\/td>\n196.6312<\/td>\n40.5818<\/td>\n12.2182<\/td>\n14.1455<\/td>\n12.2714<\/td>\n11.5338<\/td>\n12.0546<\/td>\n13.7792<\/td>\n12.8234<\/td>\n7.2948<\/td>\n3.8766<\/td>\n50.0740<\/td>\n49.9260<\/td>\n56.5662<\/td>\n<\/tr>\n
    13<\/th>\n834862<\/td>\n‘White’<\/td>\n‘MEDICARE\u00a0ADVANTAGE’<\/td>\n‘MN’<\/td>\n481<\/td>\n‘Midwest’<\/td>\n‘West\u00a0North\u00a0Central’<\/td>\n47<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘1749’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n1.2190e+04<\/td>\n249.1628<\/td>\n40.7686<\/td>\n12.8465<\/td>\n14.0198<\/td>\n10.0698<\/td>\n12.7035<\/td>\n12.9919<\/td>\n14.9977<\/td>\n12.1826<\/td>\n6.5198<\/td>\n3.6779<\/td>\n50.9942<\/td>\n49.0058<\/td>\n58.1977<\/td>\n<\/tr>\n
    14<\/th>\n834862<\/td>\n‘White’<\/td>\n‘COMMERCIAL’<\/td>\n‘MI’<\/td>\n481<\/td>\n‘Midwest’<\/td>\n‘East\u00a0North\u00a0Central’<\/td>\n47<\/td>\n‘F’<\/td>\n26<\/td>\n‘1749’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n2.3266+04<\/td>\n743.5571<\/td>\n41.4729<\/td>\n10.9443<\/td>\n13.5914<\/td>\n12.6671<\/td>\n11.6100<\/td>\n12.1371<\/td>\n14.6457<\/td>\n12.7271<\/td>\n7.9286<\/td>\n3.7514<\/td>\n49.4800<\/td>\n50.5200<\/td>\n50.2657<\/td>\n<\/tr>\n
    \u22ee<\/th>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
    <\/div>\n
    <\/div>\n
    <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    I want to see some high-level statistics about the data, so I’ll use the summary<\/span><\/a> function to get an idea of what kind of information we have.<\/div>\n
    \n
    \n
    summary(allTrainData)<\/span><\/div>\n
    \n
    \n
    \n

    Variables:<\/p>\n

    patient_id<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: patient_id
    \nValues:<\/p>\n

    Min 1.0004e+05
    \nMedian 5.5577e+05
    \nMax 9.9998e+05<\/p>\n

    patient_race<\/strong>: 13173\u00d71 cell array of character vectors<\/p>\n

    Properties:
    \nDescription: patient_race
    \npayer_type<\/strong>: 13173\u00d71 cell array of character vectors<\/p>\n

    Properties:
    \nDescription: payer_type
    \npatient_state<\/strong>: 13173\u00d71 cell array of character vectors<\/p>\n

    Properties:
    \nDescription: patient_state
    \npatient_zip3<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: patient_zip3
    \nValues:<\/p>\n

    Min 100
    \nMedian 557
    \nMax 995<\/p>\n

    Region<\/strong>: 13173\u00d71 cell array of character vectors<\/p>\n

    Properties:
    \nDescription: Region
    \nDivision<\/strong>: 13173\u00d71 cell array of character vectors<\/p>\n

    Properties:
    \nDescription: Division
    \npatient_age<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: patient_age
    \nValues:<\/p>\n

    Min 18
    \nMedian 59
    \nMax 91<\/p>\n

    patient_gender<\/strong>: 13173\u00d71 cell array of character vectors<\/p>\n

    Properties:
    \nDescription: patient_gender
    \nbmi<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: bmi
    \nValues:<\/p>\n

    Min 15
    \nMedian 28.58
    \nMax 97
    \nNumMissing 9071<\/p>\n

    breast_cancer_diagnosis_code<\/strong>: 13173\u00d71 cell array of character vectors<\/p>\n

    Properties:
    \nDescription: breast_cancer_diagnosis_code
    \nbreast_cancer_diagnosis_desc<\/strong>: 13173\u00d71 cell array of character vectors<\/p>\n

    Properties:
    \nDescription: breast_cancer_diagnosis_desc
    \nmetastatic_cancer_diagnosis_code<\/strong>: 13173\u00d71 cell array of character vectors<\/p>\n

    Properties:
    \nDescription: metastatic_cancer_diagnosis_code
    \nmetastatic_first_novel_treatment<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: metastatic_first_novel_treatment
    \nValues:<\/p>\n

    Min NaN
    \nMedian NaN
    \nMax NaN
    \nNumMissing 13173<\/p>\n

    metastatic_first_novel_treatment_type<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: metastatic_first_novel_treatment_type
    \nValues:<\/p>\n

    Min NaN
    \nMedian NaN
    \nMax NaN
    \nNumMissing 13173<\/p>\n

    population<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: population
    \nValues:<\/p>\n

    Min 635.55
    \nMedian 18953
    \nMax 71374<\/p>\n

    density<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: density
    \nValues:<\/p>\n

    Min 0.91667
    \nMedian 700.34
    \nMax 29852<\/p>\n

    age_median<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_median
    \nValues:<\/p>\n

    Min 20.6
    \nMedian 40.639
    \nMax 54.57<\/p>\n

    age_under_10<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_under_10
    \nValues:<\/p>\n

    Min 0
    \nMedian 11.004
    \nMax 17.675<\/p>\n

    age_10_to_19<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_10_to_19
    \nValues:<\/p>\n

    Min 6.3143
    \nMedian 12.898
    \nMax 35.3<\/p>\n

    age_20s<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_20s
    \nValues:<\/p>\n

    Min 5.925
    \nMedian 12.532
    \nMax 62.1<\/p>\n

    age_30s<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_30s
    \nValues:<\/p>\n

    Min 1.5
    \nMedian 12.404
    \nMax 25.471<\/p>\n

    age_40s<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_40s
    \nValues:<\/p>\n

    Min 0.8
    \nMedian 12.124
    \nMax 17.82<\/p>\n

    age_50s<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_50s
    \nValues:<\/p>\n

    Min 0
    \nMedian 13.57
    \nMax 21.661<\/p>\n

    age_60s<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_60s
    \nValues:<\/p>\n

    Min 0.2
    \nMedian 12.518
    \nMax 24.51<\/p>\n

    age_70s<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_70s
    \nValues:<\/p>\n

    Min 0
    \nMedian 7.325
    \nMax 19<\/p>\n

    age_over_80<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: age_over_80
    \nValues:<\/p>\n

    Min 0
    \nMedian 3.8246
    \nMax 18.825<\/p>\n

    male<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: male
    \nValues:<\/p>\n

    Min 39.725
    \nMedian 49.976
    \nMax 61.6<\/p>\n

    female<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: female
    \nValues:<\/p>\n

    Min 38.4
    \nMedian 50.024
    \nMax 60.275<\/p>\n

    married<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: married
    \nValues:<\/p>\n

    Min 0.9
    \nMedian 49.434
    \nMax 66.903<\/p>\n

    divorced<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: divorced
    \nValues:<\/p>\n

    Min 0.2
    \nMedian 12.717
    \nMax 21.033<\/p>\n

    never_married<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: never_married
    \nValues:<\/p>\n

    Min 13.44
    \nMedian 32.011
    \nMax 98.9<\/p>\n

    widowed<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: widowed
    \nValues:<\/p>\n

    Min 0
    \nMedian 5.5507
    \nMax 20.65<\/p>\n

    family_size<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: family_size
    \nValues:<\/p>\n

    Min 2.5504
    \nMedian 3.16
    \nMax 4.1723
    \nNumMissing 5<\/p>\n

    family_dual_income<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: family_dual_income
    \nValues:<\/p>\n

    Min 19.312
    \nMedian 52.592
    \nMax 65.635
    \nNumMissing 5<\/p>\n

    income_household_median<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_median
    \nValues:<\/p>\n

    Min 29222
    \nMedian 69730
    \nMax 1.6412e+05
    \nNumMissing 5<\/p>\n

    income_household_under_5<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_under_5
    \nValues:<\/p>\n

    Min 0.75
    \nMedian 2.8848
    \nMax 19.62
    \nNumMissing 5<\/p>\n

    income_household_5_to_10<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_5_to_10
    \nValues:<\/p>\n

    Min 0.36154
    \nMedian 2.1986
    \nMax 11.872
    \nNumMissing 5<\/p>\n

    income_household_10_to_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_10_to_15
    \nValues:<\/p>\n

    Min 1.0154
    \nMedian 3.7875
    \nMax 14.278
    \nNumMissing 5<\/p>\n

    income_household_15_to_20<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_15_to_20
    \nValues:<\/p>\n

    Min 1.0278
    \nMedian 3.7883
    \nMax 12.4
    \nNumMissing 5<\/p>\n

    income_household_20_to_25<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_20_to_25
    \nValues:<\/p>\n

    Min 1.1
    \nMedian 4.0421
    \nMax 14.35
    \nNumMissing 5<\/p>\n

    income_household_25_to_35<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_25_to_35
    \nValues:<\/p>\n

    Min 2.65
    \nMedian 8.4349
    \nMax 26.55
    \nNumMissing 5<\/p>\n

    income_household_35_to_50<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_35_to_50
    \nValues:<\/p>\n

    Min 1.7
    \nMedian 11.833
    \nMax 24.075
    \nNumMissing 5<\/p>\n

    income_household_50_to_75<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_50_to_75
    \nValues:<\/p>\n

    Min 4.95
    \nMedian 17.076
    \nMax 27.13
    \nNumMissing 5<\/p>\n

    income_household_75_to_100<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_75_to_100
    \nValues:<\/p>\n

    Min 4.7333
    \nMedian 12.677
    \nMax 24.8
    \nNumMissing 5<\/p>\n

    income_household_100_to_150<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_100_to_150
    \nValues:<\/p>\n

    Min 4.2889
    \nMedian 15.938
    \nMax 27.477
    \nNumMissing 5<\/p>\n

    income_household_150_over<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_150_over
    \nValues:<\/p>\n

    Min 0.84
    \nMedian 14.655
    \nMax 52.824
    \nNumMissing 5<\/p>\n

    income_household_six_figure<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_household_six_figure
    \nValues:<\/p>\n

    Min 5.6926
    \nMedian 30.523
    \nMax 69.032
    \nNumMissing 5<\/p>\n

    income_individual_median<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: income_individual_median
    \nValues:<\/p>\n

    Min 4316
    \nMedian 35211
    \nMax 88910<\/p>\n

    home_ownership<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: home_ownership
    \nValues:<\/p>\n

    Min 15.85
    \nMedian 69.91
    \nMax 90.367
    \nNumMissing 5<\/p>\n

    housing_units<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: housing_units
    \nValues:<\/p>\n

    Min 0
    \nMedian 6994.4
    \nMax 25923<\/p>\n

    home_value<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: home_value
    \nValues:<\/p>\n

    Min 60629
    \nMedian 2.4116e+05
    \nMax 1.8531e+06
    \nNumMissing 5<\/p>\n

    rent_median<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: rent_median
    \nValues:<\/p>\n

    Min 448.4
    \nMedian 1155.4
    \nMax 2965.2
    \nNumMissing 5<\/p>\n

    rent_burden<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: rent_burden
    \nValues:<\/p>\n

    Min 17.791
    \nMedian 30.829
    \nMax 108.6
    \nNumMissing 5<\/p>\n

    education_less_highschool<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: education_less_highschool
    \nValues:<\/p>\n

    Min 0
    \nMedian 10.745
    \nMax 34.325<\/p>\n

    education_highschool<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: education_highschool
    \nValues:<\/p>\n

    Min 0
    \nMedian 27.484
    \nMax 53.96<\/p>\n

    education_some_college<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: education_some_college
    \nValues:<\/p>\n

    Min 7.2
    \nMedian 29.286
    \nMax 50.133<\/p>\n

    education_bachelors<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: education_bachelors
    \nValues:<\/p>\n

    Min 2.4657
    \nMedian 18.871
    \nMax 41.7<\/p>\n

    education_graduate<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: education_graduate
    \nValues:<\/p>\n

    Min 2.0941
    \nMedian 10.777
    \nMax 51.84<\/p>\n

    education_college_or_above<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: education_college_or_above
    \nValues:<\/p>\n

    Min 7.0488
    \nMedian 29.793
    \nMax 77.817<\/p>\n

    education_stem_degree<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: education_stem_degree
    \nValues:<\/p>\n

    Min 23.915
    \nMedian 42.99
    \nMax 73<\/p>\n

    labor_force_participation<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: labor_force_participation
    \nValues:<\/p>\n

    Min 30.7
    \nMedian 62.778
    \nMax 78.67<\/p>\n

    unemployment_rate<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: unemployment_rate
    \nValues:<\/p>\n

    Min 0.82308
    \nMedian 5.4857
    \nMax 18.8<\/p>\n

    self_employed<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: self_employed
    \nValues:<\/p>\n

    Min 2.263
    \nMedian 12.73
    \nMax 25.538
    \nNumMissing 5<\/p>\n

    farmer<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: farmer
    \nValues:<\/p>\n

    Min 0
    \nMedian 0.45493
    \nMax 25.267
    \nNumMissing 5<\/p>\n

    race_white<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: race_white
    \nValues:<\/p>\n

    Min 14.496
    \nMedian 70.904
    \nMax 98.444<\/p>\n

    race_black<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: race_black
    \nValues:<\/p>\n

    Min 0.08
    \nMedian 6.4103
    \nMax 69.66<\/p>\n

    race_asian<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: race_asian
    \nValues:<\/p>\n

    Min 0
    \nMedian 2.8214
    \nMax 49.85<\/p>\n

    race_native<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: race_native
    \nValues:<\/p>\n

    Min 0
    \nMedian 0.42759
    \nMax 76.935<\/p>\n

    race_pacific<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: race_pacific
    \nValues:<\/p>\n

    Min 0
    \nMedian 0.05
    \nMax 14.758<\/p>\n

    race_other<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: race_other
    \nValues:<\/p>\n

    Min 0.002564
    \nMedian 3.52
    \nMax 33.189<\/p>\n

    race_multiple<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: race_multiple
    \nValues:<\/p>\n

    Min 0.43333
    \nMedian 5.65
    \nMax 26.43<\/p>\n

    hispanic<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: hispanic
    \nValues:<\/p>\n

    Min 0.060714
    \nMedian 11.983
    \nMax 91.005<\/p>\n

    disabled<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: disabled
    \nValues:<\/p>\n

    Min 4.6
    \nMedian 12.955
    \nMax 35.156<\/p>\n

    poverty<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: poverty
    \nValues:<\/p>\n

    Min 3.4333
    \nMedian 12.209
    \nMax 38.348
    \nNumMissing 5<\/p>\n

    limited_english<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: limited_english
    \nValues:<\/p>\n

    Min 0
    \nMedian 2.7472
    \nMax 26.755
    \nNumMissing 5<\/p>\n

    commute_time<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: commute_time
    \nValues:<\/p>\n

    Min 12.461
    \nMedian 27.786
    \nMax 48.02<\/p>\n

    health_uninsured<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: health_uninsured
    \nValues:<\/p>\n

    Min 2.44
    \nMedian 7.3556
    \nMax 27.566<\/p>\n

    veteran<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: veteran
    \nValues:<\/p>\n

    Min 1.2
    \nMedian 6.9933
    \nMax 25.2<\/p>\n

    AverageOfJan_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jan-13
    \nValues:<\/p>\n

    Min 6.7891
    \nMedian 35.412
    \nMax 72.373
    \nNumMissing 33<\/p>\n

    AverageOfFeb_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Feb-13
    \nValues:<\/p>\n

    Min 8.9344
    \nMedian 36.71
    \nMax 71.003
    \nNumMissing 3<\/p>\n

    AverageOfMar_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Mar-13
    \nValues:<\/p>\n

    Min 14.001
    \nMedian 40.585
    \nMax 70.707<\/p>\n

    AverageOfApr_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Apr-13
    \nValues:<\/p>\n

    Min 29.303
    \nMedian 53.65
    \nMax 76.73<\/p>\n

    AverageOfMay_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of May-13
    \nValues:<\/p>\n

    Min 43.258
    \nMedian 63.891
    \nMax 81.449
    \nNumMissing 3<\/p>\n

    AverageOfJun_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jun-13
    \nValues:<\/p>\n

    Min 56.635
    \nMedian 71.18
    \nMax 91.641
    \nNumMissing 20<\/p>\n

    AverageOfJul_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jul-13
    \nValues:<\/p>\n

    Min 60.114
    \nMedian 74.462
    \nMax 96.454<\/p>\n

    AverageOfAug_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Aug-13
    \nValues:<\/p>\n

    Min 56.867
    \nMedian 72.511
    \nMax 92.333
    \nNumMissing 17<\/p>\n

    AverageOfSep_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Sep-13
    \nValues:<\/p>\n

    Min 48.108
    \nMedian 68.27
    \nMax 86.437
    \nNumMissing 27<\/p>\n

    AverageOfOct_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Oct-13
    \nValues:<\/p>\n

    Min 39.809
    \nMedian 57.171
    \nMax 80.183
    \nNumMissing 59<\/p>\n

    AverageOfNov_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Nov-13
    \nValues:<\/p>\n

    Min 24.242
    \nMedian 43.371
    \nMax 76.612
    \nNumMissing 3<\/p>\n

    AverageOfDec_13<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Dec-13
    \nValues:<\/p>\n

    Min -1.1231
    \nMedian 36.49
    \nMax 74.47
    \nNumMissing 3<\/p>\n

    AverageOfJan_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jan-14
    \nValues:<\/p>\n

    Min -2.863
    \nMedian 31.096
    \nMax 70.775
    \nNumMissing 4<\/p>\n

    AverageOfFeb_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Feb-14
    \nValues:<\/p>\n

    Min 0.39012
    \nMedian 34.685
    \nMax 73.245
    \nNumMissing 9<\/p>\n

    AverageOfMar_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Mar-14
    \nValues:<\/p>\n

    Min 13.962
    \nMedian 41.958
    \nMax 72.13
    \nNumMissing 29<\/p>\n

    AverageOfApr_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Apr-14
    \nValues:<\/p>\n

    Min 32.845
    \nMedian 55.348
    \nMax 76.205
    \nNumMissing 180<\/p>\n

    AverageOfMay_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of May-14
    \nValues:<\/p>\n

    Min 46.646
    \nMedian 64.027
    \nMax 80.57<\/p>\n

    AverageOfJun_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jun-14
    \nValues:<\/p>\n

    Min 51.611
    \nMedian 71.413
    \nMax 90.224
    \nNumMissing 152<\/p>\n

    AverageOfJul_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jul-14
    \nValues:<\/p>\n

    Min 57.604
    \nMedian 73.955
    \nMax 95.528<\/p>\n

    AverageOfAug_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Aug-14
    \nValues:<\/p>\n

    Min 56.561
    \nMedian 73.225
    \nMax 90.17<\/p>\n

    AverageOfSep_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Sep-14
    \nValues:<\/p>\n

    Min 42.48
    \nMedian 67.588
    \nMax 87.833<\/p>\n

    AverageOfOct_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Oct-14
    \nValues:<\/p>\n

    Min 34.796
    \nMedian 58.049
    \nMax 82.105<\/p>\n

    AverageOfNov_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Nov-14
    \nValues:<\/p>\n

    Min 19.001
    \nMedian 41.864
    \nMax 74.565
    \nNumMissing 24<\/p>\n

    AverageOfDec_14<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Dec-14
    \nValues:<\/p>\n

    Min 15.782
    \nMedian 39.631
    \nMax 72.174<\/p>\n

    AverageOfJan_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jan-15
    \nValues:<\/p>\n

    Min 9.6504
    \nMedian 34.297
    \nMax 70.595
    \nNumMissing 6<\/p>\n

    AverageOfFeb_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Feb-15
    \nValues:<\/p>\n

    Min 0.39436
    \nMedian 33.389
    \nMax 72.165
    \nNumMissing 12<\/p>\n

    AverageOfMar_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Mar-15
    \nValues:<\/p>\n

    Min 21.481
    \nMedian 45.209
    \nMax 75.841
    \nNumMissing 12<\/p>\n

    AverageOfApr_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Apr-15
    \nValues:<\/p>\n

    Min 38.365
    \nMedian 55.409
    \nMax 79.593
    \nNumMissing 28<\/p>\n

    AverageOfMay_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of May-15
    \nValues:<\/p>\n

    Min 44.952
    \nMedian 64.963
    \nMax 80.898<\/p>\n

    AverageOfJun_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jun-15
    \nValues:<\/p>\n

    Min 55.876
    \nMedian 71.144
    \nMax 92.338<\/p>\n

    AverageOfJul_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jul-15
    \nValues:<\/p>\n

    Min 58.114
    \nMedian 74.724
    \nMax 92.895<\/p>\n

    AverageOfAug_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Aug-15
    \nValues:<\/p>\n

    Min 56.368
    \nMedian 74.452
    \nMax 95.258
    \nNumMissing 22<\/p>\n

    AverageOfSep_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Sep-15
    \nValues:<\/p>\n

    Min 46.958
    \nMedian 71.177
    \nMax 98.951<\/p>\n

    AverageOfOct_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Oct-15
    \nValues:<\/p>\n

    Min 41.013
    \nMedian 57.607
    \nMax 82.79
    \nNumMissing 16<\/p>\n

    AverageOfNov_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Nov-15
    \nValues:<\/p>\n

    Min 26.877
    \nMedian 48.956
    \nMax 79.126
    \nNumMissing 16<\/p>\n

    AverageOfDec_15<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Dec-15
    \nValues:<\/p>\n

    Min 16.14
    \nMedian 46.322
    \nMax 77.383
    \nNumMissing 18<\/p>\n

    AverageOfJan_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jan-16
    \nValues:<\/p>\n

    Min 9.633
    \nMedian 33.117
    \nMax 71.904
    \nNumMissing 16<\/p>\n

    AverageOfFeb_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Feb-16
    \nValues:<\/p>\n

    Min 14.552
    \nMedian 39.459
    \nMax 77.696
    \nNumMissing 16<\/p>\n

    AverageOfMar_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Mar-16
    \nValues:<\/p>\n

    Min 29.155
    \nMedian 50.109
    \nMax 74.822<\/p>\n

    AverageOfApr_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Apr-16
    \nValues:<\/p>\n

    Min 35.264
    \nMedian 55.783
    \nMax 76.571<\/p>\n

    AverageOfMay_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of May-16
    \nValues:<\/p>\n

    Min 45.325
    \nMedian 61.856
    \nMax 79.608
    \nNumMissing 19<\/p>\n

    AverageOfJun_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jun-16
    \nValues:<\/p>\n

    Min 55.897
    \nMedian 72.583
    \nMax 94.287<\/p>\n

    AverageOfJul_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jul-16
    \nValues:<\/p>\n

    Min 60.402
    \nMedian 76.48
    \nMax 95.633
    \nNumMissing 16<\/p>\n

    AverageOfAug_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Aug-16
    \nValues:<\/p>\n

    Min 58.124
    \nMedian 76.37
    \nMax 96.091<\/p>\n

    AverageOfSep_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Sep-16
    \nValues:<\/p>\n

    Min 50.671
    \nMedian 70.889
    \nMax 85.494<\/p>\n

    AverageOfOct_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Oct-16
    \nValues:<\/p>\n

    Min 37.083
    \nMedian 60.207
    \nMax 79.631<\/p>\n

    AverageOfNov_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Nov-16
    \nValues:<\/p>\n

    Min 25.945
    \nMedian 49.15
    \nMax 75.547
    \nNumMissing 3<\/p>\n

    AverageOfDec_16<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Dec-16
    \nValues:<\/p>\n

    Min 9.8677
    \nMedian 36.823
    \nMax 75.628
    \nNumMissing 13<\/p>\n

    AverageOfJan_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jan-17
    \nValues:<\/p>\n

    Min 10.249
    \nMedian 37.942
    \nMax 71.952
    \nNumMissing 9<\/p>\n

    AverageOfFeb_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Feb-17
    \nValues:<\/p>\n

    Min 17.485
    \nMedian 44.27
    \nMax 72.402<\/p>\n

    AverageOfMar_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Mar-17
    \nValues:<\/p>\n

    Min 20.439
    \nMedian 47.794
    \nMax 73.785<\/p>\n

    AverageOfApr_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Apr-17
    \nValues:<\/p>\n

    Min 38.856
    \nMedian 57.596
    \nMax 80.696<\/p>\n

    AverageOfMay_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of May-17
    \nValues:<\/p>\n

    Min 46.06
    \nMedian 62.719
    \nMax 82.129<\/p>\n

    AverageOfJun_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jun-17
    \nValues:<\/p>\n

    Min 53.403
    \nMedian 71.213
    \nMax 92.757
    \nNumMissing 1<\/p>\n

    AverageOfJul_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jul-17
    \nValues:<\/p>\n

    Min 58.14
    \nMedian 75.782
    \nMax 106.73
    \nNumMissing 31<\/p>\n

    AverageOfAug_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Aug-17
    \nValues:<\/p>\n

    Min 55.428
    \nMedian 72.311
    \nMax 94.479<\/p>\n

    AverageOfSep_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Sep-17
    \nValues:<\/p>\n

    Min 49.352
    \nMedian 69.367
    \nMax 85.72
    \nNumMissing 10<\/p>\n

    AverageOfOct_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Oct-17
    \nValues:<\/p>\n

    Min 38.41
    \nMedian 60.651
    \nMax 79.556
    \nNumMissing 21<\/p>\n

    AverageOfNov_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Nov-17
    \nValues:<\/p>\n

    Min 23.168
    \nMedian 46.499
    \nMax 75.306
    \nNumMissing 5<\/p>\n

    AverageOfDec_17<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Dec-17
    \nValues:<\/p>\n

    Min 8.609
    \nMedian 35.899
    \nMax 71.741<\/p>\n

    AverageOfJan_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jan-18
    \nValues:<\/p>\n

    Min 5.9302
    \nMedian 33.93
    \nMax 73.314<\/p>\n

    AverageOfFeb_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Feb-18
    \nValues:<\/p>\n

    Min 4.1048
    \nMedian 42.023
    \nMax 75.045
    \nNumMissing 5<\/p>\n

    AverageOfMar_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Mar-18
    \nValues:<\/p>\n

    Min 22.722
    \nMedian 43.237
    \nMax 71.638
    \nNumMissing 6<\/p>\n

    AverageOfApr_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Apr-18
    \nValues:<\/p>\n

    Min 28.793
    \nMedian 50.292
    \nMax 76.49<\/p>\n

    AverageOfMay_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of May-18
    \nValues:<\/p>\n

    Min 45.877
    \nMedian 66.117
    \nMax 86.572<\/p>\n

    AverageOfJun_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jun-18
    \nValues:<\/p>\n

    Min 53.458
    \nMedian 71.642
    \nMax 90.658
    \nNumMissing 9<\/p>\n

    AverageOfJul_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Jul-18
    \nValues:<\/p>\n

    Min 58.542
    \nMedian 76.647
    \nMax 96.432
    \nNumMissing 46<\/p>\n

    AverageOfAug_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Aug-18
    \nValues:<\/p>\n

    Min 56.201
    \nMedian 76.079
    \nMax 95.772
    \nNumMissing 16<\/p>\n

    AverageOfSep_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Sep-18
    \nValues:<\/p>\n

    Min 51.829
    \nMedian 70.876
    \nMax 89.194
    \nNumMissing 7<\/p>\n

    AverageOfOct_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Oct-18
    \nValues:<\/p>\n

    Min 37.539
    \nMedian 57.454
    \nMax 81.46
    \nNumMissing 7<\/p>\n

    AverageOfNov_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Nov-18
    \nValues:<\/p>\n

    Min 19.145
    \nMedian 42.426
    \nMax 76.301
    \nNumMissing 12<\/p>\n

    AverageOfDec_18<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: Average of Dec-18
    \nValues:<\/p>\n

    Min 15.377
    \nMedian 38.496
    \nMax 73.539
    \nNumMissing 33<\/p>\n

    metastatic_diagnosis_period<\/strong>: 13173\u00d71 double<\/p>\n

    Properties:
    \nDescription: metastatic_diagnosis_period
    \nValues:<\/p>\n

    Min 0
    \nMedian 44
    \nMax 365<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

    Take some time to scroll through this summary and see what information or patterns you can learn! Here are some things I notice:<\/div>\n
      \n
    1. There are a lot of rows or variables<\/span> that just say “cell array of character vectors”, which doesn’t tell us much about the data.<\/li>\n
    2. There are a few variables that have a high ‘NumMissing’ value.<\/li>\n
    3. The numeric variables can have dramatically different minimums and maximums.<\/li>\n<\/ol>\n
      We can use these observations to make decisions about how we want to explore and preprocess the dataset.<\/div>\n

      Process and Clean the Data<\/h2>\n

      1. Convert text data to categorical<\/h3>\n
      Text data can be hard for machine learning algorithms to understand, so let’s go through and change each “cell array of character vectors” to a categorical<\/span><\/a>. This will help the algorithm sort the text into different categories instead of understanding it as a series of individual letters.<\/div>\n
      \n
      \n
      varTypes = varfun(@class, allTrainData, OutputFormat=“cell”<\/span>);<\/span><\/div>\n<\/div>\n
      \n
      catIdx = strcmp(varTypes, “cell”<\/span>);<\/span><\/div>\n<\/div>\n
      \n
      varNames = allTrainData.Properties.VariableNames;<\/span><\/div>\n<\/div>\n
      \n
      catVarNames = varNames(catIdx);<\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      for <\/span>catNameIdx = 1:length(catVarNames)<\/span><\/div>\n<\/div>\n
      \n
      allTrainData.(catVarNames{catNameIdx}) = categorical(allTrainData.(catVarNames{catNameIdx}));<\/span><\/div>\n<\/div>\n
      \n
      end<\/span><\/span><\/div>\n<\/div>\n<\/div>\n

      2. Handle Missing Data<\/h3>\n
      Now I want to handle all that missing data I noticed earlier. I’ll go through each variable and specifically look at variables that are missing data for over half of the rows or observations<\/span>.<\/div>\n
      \n
      \n
      dataSum = summary(allTrainData);<\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      for <\/span>nameIdx = 1:length(varNames)<\/span><\/div>\n<\/div>\n
      \n
      varName = varNames{nameIdx};<\/span><\/div>\n<\/div>\n
      \n
      varNumMissing = dataSum.(varName).NumMissing;<\/span><\/div>\n<\/div>\n
      \n
      if <\/span>varNumMissing > (height(allTrainData) \/ 2)<\/span><\/div>\n<\/div>\n
      \n
      disp(varName);<\/span><\/div>\n<\/div>\n
      \n
      disp(varNumMissing);<\/span><\/div>\n<\/div>\n
      \n
      end<\/span><\/span><\/div>\n<\/div>\n
      \n
      end<\/span><\/span><\/div>\n
      \n
      \n
      patient_race<\/div>\n<\/div>\n
      \n
      6657<\/div>\n<\/div>\n
      \n
      bmi<\/div>\n<\/div>\n
      \n
      9071<\/div>\n<\/div>\n
      \n
      metastatic_first_novel_treatment<\/div>\n<\/div>\n
      \n
      13173<\/div>\n<\/div>\n
      \n
      metastatic_first_novel_treatment_type<\/div>\n<\/div>\n
      \n
      13173<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
      Let’s remove those variables entirely, since they might not be too helpful for our algorithm.<\/div>\n
      \n
      \n
      allTrainData = removevars(allTrainData, [“patient_race”<\/span>, “bmi”<\/span>, “metastatic_first_novel_treatment”<\/span>, “metastatic_first_novel_treatment_type”<\/span>])<\/span><\/div>\n
      \n
      \n
      \n
      allTrainData = 13173\u00d7148 table <\/span><\/div>\n
      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      <\/th>\npatient_id<\/th>\npayer_type<\/th>\npatient_state<\/th>\npatient_zip3<\/th>\nRegion<\/th>\nDivision<\/th>\npatient_age<\/th>\npatient_gender<\/th>\nbreast_cancer_diagnosis_code<\/th>\nbreast_cancer_diagnosis_desc<\/th>\nmetastatic_cancer_diagnosis_code<\/th>\npopulation<\/th>\ndensity<\/th>\nage_median<\/th>\nage_under_10<\/th>\nage_10_to_19<\/th>\nage_20s<\/th>\nage_30s<\/th>\nage_40s<\/th>\nage_50s<\/th>\nage_60s<\/th>\nage_70s<\/th>\nage_over_80<\/th>\nmale<\/th>\nfemale<\/th>\nmarried<\/th>\ndivorced<\/th>\nnever_married<\/th>\nwidowed<\/th>\nfamily_size<\/th>\n<\/tr>\n<\/thead>\n
      1<\/th>\n268700<\/td>\nCOMMERCIAL<\/td>\nAR<\/td>\n724<\/td>\nSouth<\/td>\nWest\u00a0South\u00a0Central<\/td>\n39<\/td>\nF<\/td>\nC50912<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC773<\/td>\n3.9249e+03<\/td>\n82.6283<\/td>\n42.5750<\/td>\n11.6050<\/td>\n13.0317<\/td>\n10.8667<\/td>\n11.8017<\/td>\n12.2917<\/td>\n13.2167<\/td>\n13.4717<\/td>\n10.0717<\/td>\n3.6350<\/td>\n51.4317<\/td>\n48.5683<\/td>\n51.0483<\/td>\n16.7233<\/td>\n23.5650<\/td>\n8.6550<\/td>\n3.0093<\/td>\n<\/tr>\n
      2<\/th>\n484983<\/td>\n<undefined><\/td>\nIL<\/td>\n629<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n55<\/td>\nF<\/td>\nC50412<\/td>\nMalig\u00a0neoplasm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC773<\/td>\n2.7454e+03<\/td>\n51.7936<\/td>\n43.5351<\/td>\n11.2247<\/td>\n12.1922<\/td>\n11.4467<\/td>\n11.0065<\/td>\n11.3545<\/td>\n14.3922<\/td>\n14.1507<\/td>\n9.1727<\/td>\n5.0506<\/td>\n49.3234<\/td>\n50.6766<\/td>\n49.4753<\/td>\n15.4182<\/td>\n26.9286<\/td>\n8.1714<\/td>\n3.1749<\/td>\n<\/tr>\n
      3<\/th>\n277055<\/td>\nCOMMERCIAL<\/td>\nCA<\/td>\nundefined<\/td>\nWest<\/td>\nPacific<\/td>\n59<\/td>\nF<\/td>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\nC773<\/td>\n3.8343e+04<\/td>\n700.3375<\/td>\n36.2795<\/td>\n13.2667<\/td>\n15.6641<\/td>\n13.4949<\/td>\n13.4538<\/td>\n12.4000<\/td>\n11.5846<\/td>\n10.4667<\/td>\n6.3769<\/td>\n3.2846<\/td>\n49.9897<\/td>\n50.0103<\/td>\n48.8077<\/td>\n11.8974<\/td>\n34.3487<\/td>\n4.9487<\/td>\n3.7977<\/td>\n<\/tr>\n
      4<\/th>\n320055<\/td>\nMEDICAID<\/td>\nCA<\/td>\n925<\/td>\nWest<\/td>\nPacific<\/td>\n59<\/td>\nF<\/td>\nC50911<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\nC773<\/td>\n3.6054e+04<\/td>\n5.2943e+03<\/td>\n36.6538<\/td>\n9.7615<\/td>\n11.2677<\/td>\n17.2339<\/td>\n17.4415<\/td>\n13.0908<\/td>\n12.3046<\/td>\n9.4077<\/td>\n5.6738<\/td>\n3.8246<\/td>\n50.5108<\/td>\n49.4892<\/td>\n33.4785<\/td>\n11.3015<\/td>\n50.4569<\/td>\n4.7662<\/td>\n3.4429<\/td>\n<\/tr>\n
      5<\/th>\n190386<\/td>\nCOMMERCIAL<\/td>\nCA<\/td>\n934<\/td>\nWest<\/td>\nPacific<\/td>\n71<\/td>\nF<\/td>\n1748<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0other\u00a0specified\u00a0sites\u00a0of\u00a0female\u00a0breast<\/td>\nC7951<\/td>\n1.3700e+04<\/td>\n400.4763<\/td>\n41.7816<\/td>\n10.0316<\/td>\n16.4342<\/td>\n12.9710<\/td>\n11.2921<\/td>\n10.0868<\/td>\n11.5605<\/td>\n13.2790<\/td>\n8.7842<\/td>\n5.5316<\/td>\n51.9895<\/td>\n48.0132<\/td>\n48.2079<\/td>\n11.1632<\/td>\n35.6026<\/td>\n5.0132<\/td>\n3.0909<\/td>\n<\/tr>\n
      6<\/th>\n559027<\/td>\nCOMMERCIAL<\/td>\nIN<\/td>\n461<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n63<\/td>\nF<\/td>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\nC786<\/td>\n9.3229e+03<\/td>\n274.7371<\/td>\n40.1237<\/td>\n12.2300<\/td>\n13.8800<\/td>\n11.5317<\/td>\n11.9350<\/td>\n12.5517<\/td>\n13.9117<\/td>\n13.0467<\/td>\n7<\/td>\n3.9067<\/td>\n50.9817<\/td>\n49.0183<\/td>\n57.1617<\/td>\n12.7767<\/td>\n23.5267<\/td>\n6.5333<\/td>\n3.1912<\/td>\n<\/tr>\n
      7<\/th>\n293747<\/td>\nMEDICARE\u00a0ADVANTAGE<\/td>\nOH<\/td>\n448<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n57<\/td>\nF<\/td>\nC50412<\/td>\nMalig\u00a0neoplasm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC799<\/td>\n5.8906e+03<\/td>\n122.3929<\/td>\n42.4536<\/td>\n12.4286<\/td>\n13.1893<\/td>\n10.8089<\/td>\n10.7321<\/td>\n13.0411<\/td>\n13.2786<\/td>\n14.2804<\/td>\n7.5732<\/td>\n4.6786<\/td>\n49.9107<\/td>\n50.0893<\/td>\n55.8696<\/td>\n12.4232<\/td>\n24.4518<\/td>\n7.2625<\/td>\n2.9912<\/td>\n<\/tr>\n
      8<\/th>\n517596<\/td>\nCOMMERCIAL<\/td>\nDE<\/td>\n198<\/td>\nSouth<\/td>\nSouth\u00a0Atlantic<\/td>\n56<\/td>\nF<\/td>\nC50411<\/td>\nMalig\u00a0neoplm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\nC792<\/td>\n2.2036e+04<\/td>\n1.4505e+03<\/td>\n41.6300<\/td>\n11.0300<\/td>\n11.9800<\/td>\n12.1100<\/td>\n13.6900<\/td>\n11.6600<\/td>\n13.9500<\/td>\n12.9700<\/td>\n7.6900<\/td>\n4.9700<\/td>\n47.8200<\/td>\n52.1800<\/td>\n42.0300<\/td>\n13.3700<\/td>\n38.5100<\/td>\n6.1000<\/td>\n3.0850<\/td>\n<\/tr>\n
      9<\/th>\n533188<\/td>\nCOMMERCIAL<\/td>\nLA<\/td>\n706<\/td>\nSouth<\/td>\nWest\u00a0South\u00a0Central<\/td>\n65<\/td>\nF<\/td>\nC50212<\/td>\nMalig\u00a0neoplasm\u00a0of\u00a0upper-inner\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC773<\/td>\n7.2198e+03<\/td>\n531.0590<\/td>\n39.5421<\/td>\n12.4474<\/td>\n14.7868<\/td>\n11.0026<\/td>\n12.5368<\/td>\n11.6868<\/td>\n14.6947<\/td>\n12.4789<\/td>\n5.8816<\/td>\n4.4947<\/td>\n50.5500<\/td>\n49.4500<\/td>\n49.0737<\/td>\n17.2132<\/td>\n27.2553<\/td>\n6.4711<\/td>\n3.2531<\/td>\n<\/tr>\n
      10<\/th>\n639484<\/td>\nCOMMERCIAL<\/td>\nCA<\/td>\n922<\/td>\nWest<\/td>\nPacific<\/td>\n60<\/td>\nF<\/td>\nC50912<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC773<\/td>\n1.6550e+04<\/td>\n245.0979<\/td>\n44.2326<\/td>\n9.8872<\/td>\n10.4149<\/td>\n13.6723<\/td>\n11.3894<\/td>\n9.1447<\/td>\n15.5638<\/td>\n14.6277<\/td>\n10.1106<\/td>\n5.2298<\/td>\n54.2000<\/td>\n45.8000<\/td>\n46.5192<\/td>\n13.1872<\/td>\n33.9894<\/td>\n6.2957<\/td>\n3.3669<\/td>\n<\/tr>\n
      11<\/th>\n366431<\/td>\nMEDICARE\u00a0ADVANTAGE<\/td>\nPA<\/td>\n191<\/td>\nNortheast<\/td>\nMiddle\u00a0Atlantic<\/td>\n71<\/td>\nF<\/td>\nC50911<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\nC7989<\/td>\n3.1948e+04<\/td>\n5.5122e+03<\/td>\n35.7191<\/td>\n10.8532<\/td>\n10.9511<\/td>\n18.1596<\/td>\n17.3489<\/td>\n11.6468<\/td>\n11.0979<\/td>\n10.6425<\/td>\n5.9426<\/td>\n3.3511<\/td>\n48.3085<\/td>\n51.6915<\/td>\n32.4915<\/td>\n12.3021<\/td>\n49.7702<\/td>\n5.4298<\/td>\n3.0866<\/td>\n<\/tr>\n
      12<\/th>\n793091<\/td>\nMEDICARE\u00a0ADVANTAGE<\/td>\nOH<\/td>\n453<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n73<\/td>\nF<\/td>\nC50811<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0ovrlp\u00a0sites\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\nC773<\/td>\n6.4682e+03<\/td>\n196.6312<\/td>\n40.5818<\/td>\n12.2182<\/td>\n14.1455<\/td>\n12.2714<\/td>\n11.5338<\/td>\n12.0546<\/td>\n13.7792<\/td>\n12.8234<\/td>\n7.2948<\/td>\n3.8766<\/td>\n50.0740<\/td>\n49.9260<\/td>\n56.5662<\/td>\n11.9610<\/td>\n25.8195<\/td>\n5.6506<\/td>\n3.0866<\/td>\n<\/tr>\n
      13<\/th>\n942172<\/td>\nMEDICARE\u00a0ADVANTAGE<\/td>\nMN<\/td>\n553<\/td>\nMidwest<\/td>\nWest\u00a0North\u00a0Central<\/td>\n73<\/td>\nF<\/td>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\nC773<\/td>\n1.2190e+04<\/td>\n249.1628<\/td>\n40.7686<\/td>\n12.8465<\/td>\n14.0198<\/td>\n10.0698<\/td>\n12.7035<\/td>\n12.9919<\/td>\n14.9977<\/td>\n12.1826<\/td>\n6.5198<\/td>\n3.6779<\/td>\n50.9942<\/td>\n49.0058<\/td>\n58.1977<\/td>\n10.6512<\/td>\n26.5395<\/td>\n4.6081<\/td>\n3.1352<\/td>\n<\/tr>\n
      14<\/th>\n834862<\/td>\nCOMMERCIAL<\/td>\nMI<\/td>\n481<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n47<\/td>\nF<\/td>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\nC773<\/td>\n2.3266e+04<\/td>\n743.5571<\/td>\n41.4729<\/td>\n10.9443<\/td>\n13.5914<\/td>\n12.6671<\/td>\n11.6100<\/td>\n12.1371<\/td>\n14.6457<\/td>\n12.7271<\/td>\n7.9286<\/td>\n3.7514<\/td>\n49.4800<\/td>\n50.5200<\/td>\n50.2657<\/td>\n11.7486<\/td>\n32.4871<\/td>\n5.5043<\/td>\n3.1332<\/td>\n<\/tr>\n
      \u22ee<\/th>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
      <\/div>\n
      <\/div>\n
      <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
      Now I want to look at each row and remove any that are missing too many values. It’s okay to have a couple of missing data points in your dataset, but if you have too many it could cause your machine learning algorithm to be less accurate. I’ll use the Clean Missing Data<\/a> live task to remove any rows that are missing 2 or more data points.<\/div>\n
      \n
      \n
      % Remove missing data<\/span><\/span><\/div>\n<\/div>\n
      \n
      [cleanTrainData,missingIndices] = rmmissing(allTrainData,“MinNumMissing”<\/span>,2);<\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      % Display results<\/span><\/span><\/div>\n<\/div>\n
      \n
      figure<\/span><\/div>\n<\/div>\n
      \n
      % Get locations of missing data<\/span><\/span><\/div>\n<\/div>\n
      \n
      indicesForPlot = ismissing(allTrainData.patient_id);<\/span><\/div>\n<\/div>\n
      \n
      mask = missingIndices & ~indicesForPlot;<\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      % Plot cleaned data<\/span><\/span><\/div>\n<\/div>\n
      \n
      plot(find(~missingIndices),cleanTrainData.patient_id,“SeriesIndex”<\/span>,1,“LineWidth”<\/span>,1.5, …<\/span><\/span><\/div>\n<\/div>\n
      \n
      “DisplayName”<\/span>,“Cleaned data”<\/span>)<\/span><\/div>\n<\/div>\n
      \n
      hold on<\/span><\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      % Plot data in rows where other variables contain missing entries<\/span><\/span><\/div>\n<\/div>\n
      \n
      plot(find(mask),allTrainData.patient_id(mask),“x”<\/span>,“SeriesIndex”<\/span>,“none”<\/span>, …<\/span><\/span><\/div>\n<\/div>\n
      \n
      “DisplayName”<\/span>,“Removed by other variables”<\/span>)<\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      % Plot removed missing entries<\/span><\/span><\/div>\n<\/div>\n
      \n
      x = repelem(find(indicesForPlot),3);<\/span><\/div>\n<\/div>\n
      \n
      y = repmat([ylim(gca) missing]’,nnz(indicesForPlot),1);<\/span><\/div>\n<\/div>\n
      \n
      plot(x,y,“Color”<\/span>,[145 145 145]\/255,“DisplayName”<\/span>,“Removed missing entries”<\/span>)<\/span><\/div>\n<\/div>\n
      \n
      title(“Number of removed missing entries: ” <\/span>+ nnz(indicesForPlot))<\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      hold off<\/span><\/span><\/div>\n<\/div>\n
      \n
      legend<\/span><\/div>\n<\/div>\n
      \n
      ylabel(“patient_id”<\/span>,“Interpreter”<\/span>,“none”<\/span>)<\/span><\/div>\n
      \n
      \n
      <\/div>\n
      <\/div>\n
      <\/div>\n
      <\/div>\n<\/div>\n<\/div>\n<\/div>\n
      \n
      clear indicesForPlot mask x y<\/span><\/span><\/div>\n<\/div>\n<\/div>\n

      Explore the Data<\/h2>\n
      Now that the data is cleaned up, you should spend some time exploring your data to understand how different variables may interact with each other.<\/div>\n

      Visual Analysis – Univariate Data<\/h3>\n
      I’ll start by using the kde<\/span><\/a> function to calculate and visualize the kernel density estimate (kde) for individual variables in our dataset. This shows us how the data in that variable is distributed, similar to a histogram<\/a>, but smooths out the visualization to make it easier to understand the overall distribution and patterns without getting distracted by potential outliers.<\/div>\n
      I’ll start by visualizing the distribution of patient age to gain a better understanding of the patient data we are working with.<\/div>\n
      \n
      \n
      whichColumn = cleanTrainData.patient_age; % Modify this line to explore other variables<\/span><\/span><\/div>\n<\/div>\n
      \n
      [estProbDist,evalPoints] = kde(whichColumn);<\/span><\/div>\n<\/div>\n
      \n
      plot(evalPoints, estProbDist);<\/span><\/div>\n
      \n
      \n
      <\/div>\n
      <\/div>\n
      <\/div>\n
      <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
      Here we can see that a majority of our patients center around the 60 years old mark, with a few smaller spikes in the 80- and 90-year range. Visualizing your data like this can help you understand where they may be potential gaps in the data or identify patterns in patients who have been diagnosed with metastatic breast cancer.<\/div>\n

      Visual Analysis – Bivariate Data<\/h3>\n
      You can use the Create Plot live task<\/a> to create scatter plots of the different variables against how long it took for the patient to receive a diagnosis. Here, I’ve plotted ‘breast_cancer_diagnosis_code’ because I noticed most of the codes tend to skew left, meaning they have earlier diagnoses, but some of the codes, such as 1748, skew to the right, indicating that there may be a relationship between diagnosis code and time to diagnosis.<\/div>\n
      \n
      \n
      % Create scatter of selected data<\/span><\/span><\/div>\n<\/div>\n
      \n
      s = scatter(cleanTrainData,“metastatic_diagnosis_period”<\/span>,“breast_cancer_diagnosis_code”<\/span>,“DisplayName”<\/span>,“breast_cancer_diagnosis_code”<\/span>);<\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      % Add xlabel, ylabel, title, and legend<\/span><\/span><\/div>\n<\/div>\n
      \n
      xlabel(“metastatic_diagnosis_period”<\/span>)<\/span><\/div>\n<\/div>\n
      \n
      ylabel(“breast_cancer_diagnosis_code”<\/span>)<\/span><\/div>\n<\/div>\n
      \n
      title(“breast_cancer_diagnosis_code vs. metastatic_diagnosis_period”<\/span>)<\/span><\/div>\n<\/div>\n
      \n
      legend<\/span><\/div>\n
      \n
      \n
      <\/div>\n
      <\/div>\n
      <\/div>\n
      <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
      Take some time to explore these visualizations on your own! This live task allows you to create a variety of different plots, and you can even add multiple plots to the same axes.<\/div>\n

      Statistical Analysis<\/h3>\n
      You can also create meaningful deductions or additional data by calculating various statistics from your data. For example, let’s add a column that shows how far the patients age is away from the mean age of all patients.<\/div>\n
      \n
      \n
      meanAge = mean(cleanTrainData.patient_age);<\/span><\/div>\n<\/div>\n
      \n
      yearsFromMeanAge = cleanTrainData.patient_age – meanAge;<\/span><\/div>\n<\/div>\n
      \n
      cleanTrainData = addvars(cleanTrainData, yearsFromMeanAge, ‘Before’<\/span>, ‘metastatic_diagnosis_period’<\/span>)<\/span><\/div>\n
      \n
      \n
      \n
      cleanTrainData = 12844\u00d7149 table <\/span><\/div>\n
      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      <\/th>\npatient_id<\/th>\npayer_type<\/th>\npatient_state<\/th>\npatient_zip3<\/th>\nRegion<\/th>\nDivision<\/th>\npatient_age<\/th>\npatient_gender<\/th>\nbreast_cancer_diagnosis_code<\/th>\nbreast_cancer_diagnosis_desc<\/th>\nmetastatic_cancer_diagnosis_code<\/th>\npopulation<\/th>\ndensity<\/th>\nage_median<\/th>\nage_under_10<\/th>\nage_10_to_19<\/th>\nage_20s<\/th>\nage_30s<\/th>\nage_40s<\/th>\nage_50s<\/th>\nage_60s<\/th>\nage_70s<\/th>\nage_over_80<\/th>\nmale<\/th>\nfemale<\/th>\nmarried<\/th>\ndivorced<\/th>\nnever_married<\/th>\nwidowed<\/th>\nfamily_size<\/th>\n<\/tr>\n<\/thead>\n
      1<\/th>\n268700<\/td>\nCOMMERCIAL<\/td>\nAR<\/td>\n724<\/td>\nSouth<\/td>\nWest\u00a0South\u00a0Central<\/td>\n39<\/td>\nF<\/td>\nC50912<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC773<\/td>\n3.9249e+03<\/td>\n82.6283<\/td>\n42.5750<\/td>\n11.6050<\/td>\n13.0317<\/td>\n10.8667<\/td>\n11.8017<\/td>\n12.2917<\/td>\n13.2167<\/td>\n13.4717<\/td>\n10.0717<\/td>\n3.6350<\/td>\n51.4317<\/td>\n48.5683<\/td>\n51.0483<\/td>\n16.7233<\/td>\n23.5650<\/td>\n8.6550<\/td>\n3.0093<\/td>\n<\/tr>\n
      2<\/th>\n484983<\/td>\n<undefined><\/td>\nIL<\/td>\n629<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n55<\/td>\nF<\/td>\nC50412<\/td>\nMalig\u00a0neoplasm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC773<\/td>\n2.7454e+03<\/td>\n51.7936<\/td>\n43.5351<\/td>\n11.2247<\/td>\n12.1922<\/td>\n11.4467<\/td>\n11.0065<\/td>\n11.3545<\/td>\n14.3922<\/td>\n14.1507<\/td>\n9.1727<\/td>\n5.0506<\/td>\n49.3234<\/td>\n50.6766<\/td>\n49.4753<\/td>\n15.4182<\/td>\n26.9286<\/td>\n8.1714<\/td>\n3.1749<\/td>\n<\/tr>\n
      3<\/th>\n277055<\/td>\nCOMMERCIAL<\/td>\nCA<\/td>\n925<\/td>\nWest<\/td>\nPacific<\/td>\n59<\/td>\nF<\/td>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\nC773<\/td>\n3.8343e+04<\/td>\n700.3375<\/td>\n36.2795<\/td>\n13.2667<\/td>\n15.6641<\/td>\n13.4949<\/td>\n13.4538<\/td>\n12.4000<\/td>\n11.5846<\/td>\n10.4667<\/td>\n6.3769<\/td>\n3.2846<\/td>\n49.9897<\/td>\n50.0103<\/td>\n48.8077<\/td>\n11.8974<\/td>\n34.3487<\/td>\n4.9487<\/td>\n3.7977<\/td>\n<\/tr>\n
      4<\/th>\n320055<\/td>\nMEDICAID<\/td>\nCA<\/td>\n900<\/td>\nWest<\/td>\nPacific<\/td>\n59<\/td>\nF<\/td>\nC50911<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\nC773<\/td>\n3.6054e+04<\/td>\n5.2943e+03<\/td>\n36.6538<\/td>\n9.7615<\/td>\n11.2677<\/td>\n17.2339<\/td>\n17.4415<\/td>\n13.0908<\/td>\n12.3046<\/td>\n9.4077<\/td>\n5.6738<\/td>\n3.8246<\/td>\n50.5108<\/td>\n49.4892<\/td>\n33.4785<\/td>\n11.3015<\/td>\n50.4569<\/td>\n4.7662<\/td>\n3.4429<\/td>\n<\/tr>\n
      5<\/th>\n190386<\/td>\nCOMMERCIAL<\/td>\nCA<\/td>\n934<\/td>\nWest<\/td>\nPacific<\/td>\n71<\/td>\nF<\/td>\n1748<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0other\u00a0specified\u00a0sites\u00a0of\u00a0female\u00a0breast<\/td>\nC7951<\/td>\n1.3700e+04<\/td>\n400.4763<\/td>\n41.7816<\/td>\n10.0316<\/td>\n16.4342<\/td>\n12.9710<\/td>\n11.2921<\/td>\n10.0868<\/td>\n11.5605<\/td>\n13.2790<\/td>\n8.7842<\/td>\n5.5316<\/td>\n51.9895<\/td>\n48.0132<\/td>\n48.2079<\/td>\n11.1632<\/td>\n35.6026<\/td>\n5.0132<\/td>\n3.0909<\/td>\n<\/tr>\n
      6<\/th>\n559027<\/td>\nCOMMERCIAL<\/td>\nIN<\/td>\n461<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n63<\/td>\nF<\/td>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\nC786<\/td>\n9.3229e+03<\/td>\n274.7371<\/td>\n40.1237<\/td>\n12.2300<\/td>\n13.8800<\/td>\n11.5317<\/td>\n11.9350<\/td>\n12.5517<\/td>\n13.9117<\/td>\n13.0467<\/td>\n7<\/td>\n3.9067<\/td>\n50.9817<\/td>\n49.0183<\/td>\n57.1617<\/td>\n12.7767<\/td>\n23.5267<\/td>\n6.5333<\/td>\n3.1912<\/td>\n<\/tr>\n
      7<\/th>\n293747<\/td>\nMEDICARE\u00a0ADVANTAGE<\/td>\nOH<\/td>\n448<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n57<\/td>\nF<\/td>\nC50412<\/td>\nMalig\u00a0neoplasm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC799<\/td>\n5.8906e+03<\/td>\n122.3929<\/td>\n42.4536<\/td>\n12.4286<\/td>\n13.1893<\/td>\n10.8089<\/td>\n10.7321<\/td>\n13.0411<\/td>\n13.2786<\/td>\n14.2804<\/td>\n7.5732<\/td>\n4.6786<\/td>\n49.9107<\/td>\n50.0893<\/td>\n55.8696<\/td>\n12.4232<\/td>\n24.4518<\/td>\n7.2625<\/td>\n2.9912<\/td>\n<\/tr>\n
      8<\/th>\n517596<\/td>\nCOMMERCIAL<\/td>\nDE<\/td>\n198<\/td>\nSouth<\/td>\nSouth\u00a0Atlantic<\/td>\n56<\/td>\nF<\/td>\nC50411<\/td>\nMalig\u00a0neoplm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\nC792<\/td>\n2.2036e+04<\/td>\n1.4505e+03<\/td>\n41.6300<\/td>\n11.0300<\/td>\n11.9800<\/td>\n12.1100<\/td>\n13.6900<\/td>\n11.6600<\/td>\n13.9500<\/td>\n12.9700<\/td>\n7.6900<\/td>\n4.9700<\/td>\n47.8200<\/td>\n52.1800<\/td>\n42.0300<\/td>\n13.3700<\/td>\n38.5100<\/td>\n6.1000<\/td>\n3.0850<\/td>\n<\/tr>\n
      9<\/th>\n533188<\/td>\nCOMMERCIAL<\/td>\nLA<\/td>\n706<\/td>\nSouth<\/td>\nWest\u00a0South\u00a0Central<\/td>\n65<\/td>\nF<\/td>\nC50212<\/td>\nMalig\u00a0neoplasm\u00a0of\u00a0upper-inner\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC773<\/td>\n7.2198e+03<\/td>\n531.0590<\/td>\n39.5421<\/td>\n12.4474<\/td>\n14.7868<\/td>\n11.0026<\/td>\n12.5368<\/td>\n11.6868<\/td>\n14.6947<\/td>\n12.4789<\/td>\n5.8816<\/td>\n4.4947<\/td>\n50.5500<\/td>\n49.4500<\/td>\n49.0737<\/td>\n17.2132<\/td>\n27.2553<\/td>\n6.4711<\/td>\n3.2531<\/td>\n<\/tr>\n
      10<\/th>\n639484<\/td>\nCOMMERCIAL<\/td>\nCA<\/td>\n922<\/td>\nWest<\/td>\nPacific<\/td>\n60<\/td>\nF<\/td>\nC50912<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\nC773<\/td>\n1.6550e+04<\/td>\n245.0979<\/td>\n44.2326<\/td>\n9.8872<\/td>\n10.4149<\/td>\n13.6723<\/td>\n11.3894<\/td>\n9.1447<\/td>\n15.5638<\/td>\n14.6277<\/td>\n10.1106<\/td>\n5.2298<\/td>\n54.2000<\/td>\n45.8000<\/td>\n46.5192<\/td>\n13.1872<\/td>\n33.9894<\/td>\n6.2957<\/td>\n3.3669<\/td>\n<\/tr>\n
      11<\/th>\n366431<\/td>\nMEDICARE\u00a0ADVANTAGE<\/td>\nPA<\/td>\n191<\/td>\nNortheast<\/td>\nMiddle\u00a0Atlantic<\/td>\n71<\/td>\nF<\/td>\nC50911<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\nC7989<\/td>\n3.1948e+04<\/td>\n5.5122e+03<\/td>\n35.7191<\/td>\n10.8532<\/td>\n10.9511<\/td>\n18.1596<\/td>\n17.3489<\/td>\n11.6468<\/td>\n11.0979<\/td>\n10.6425<\/td>\n5.9426<\/td>\n3.3511<\/td>\n48.3085<\/td>\n51.6915<\/td>\n32.4915<\/td>\n12.3021<\/td>\n49.7702<\/td>\n5.4298<\/td>\n3.0866<\/td>\n<\/tr>\n
      12<\/th>\n793091<\/td>\nMEDICARE\u00a0ADVANTAGE<\/td>\nOH<\/td>\n453<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n73<\/td>\nF<\/td>\nC50811<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0ovrlp\u00a0sites\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\nC773<\/td>\n6.4682e+03<\/td>\n196.6312<\/td>\n40.5818<\/td>\n12.2182<\/td>\n14.1455<\/td>\n12.2714<\/td>\n11.5338<\/td>\n12.0546<\/td>\n13.7792<\/td>\n12.8234<\/td>\n7.2948<\/td>\n3.8766<\/td>\n50.0740<\/td>\n49.9260<\/td>\n56.5662<\/td>\n11.9610<\/td>\n25.8195<\/td>\n5.6506<\/td>\n3.0866<\/td>\n<\/tr>\n
      13<\/th>\n942172<\/td>\nMEDICARE\u00a0ADVANTAGE<\/td>\nMN<\/td>\n553<\/td>\nMidwest<\/td>\nWest\u00a0North\u00a0Central<\/td>\n73<\/td>\nF<\/td>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\nC773<\/td>\n1.2190e+04<\/td>\n249.1628<\/td>\n40.7686<\/td>\n12.8465<\/td>\n14.0198<\/td>\n10.0698<\/td>\n12.7035<\/td>\n12.9919<\/td>\n14.9977<\/td>\n12.1826<\/td>\n6.5198<\/td>\n3.6779<\/td>\n50.9942<\/td>\n49.0058<\/td>\n58.1977<\/td>\n10.6512<\/td>\n26.5395<\/td>\n4.6081<\/td>\n3.1352<\/td>\n<\/tr>\n
      14<\/th>\n834862<\/td>\nCOMMERCIAL<\/td>\nMI<\/td>\n481<\/td>\nMidwest<\/td>\nEast\u00a0North\u00a0Central<\/td>\n47<\/td>\nF<\/td>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\nC773<\/td>\n2.3266e+04<\/td>\n743.5571<\/td>\n41.4729<\/td>\n10.9443<\/td>\n13.5914<\/td>\n12.6671<\/td>\n11.6100<\/td>\n12.1371<\/td>\n14.6457<\/td>\n12.7271<\/td>\n7.9286<\/td>\n3.7514<\/td>\n49.4800<\/td>\n50.5200<\/td>\n50.2657<\/td>\n11.7486<\/td>\n32.4871<\/td>\n5.5043<\/td>\n3.1332<\/td>\n<\/tr>\n
      \u22ee<\/th>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
      <\/div>\n
      <\/div>\n
      <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
      If you scroll all the way to the right of this table, you’ll see a new column called ‘yearsFromMeanAge’ that contains the data we just created! This is just a simple example, but it should give you an idea of how you can investigate and augment your data.<\/div>\n

      Feature Engineering<\/h2>\n
      When it comes to machine learning, you don\u2019t have to use all of the data as it is presented to you. Feature Engineering<\/a> is the process of deciding what data you want to use, creating new data based on the provided data, and transforming the data to be in whatever format or range is suitable for your workflow. You can do this manually, and some of the exploration we just did should influence decisions you make if you want to play around with including or excluding different variables.<\/div>\n
      For this blog, I\u2019ll use the genrfeatures<\/span><\/a> function to automate this process. I want to use 30 features, so MATLAB will go through and create a set of meaningful features based on our processed dataset. It may keep some data as-is, but will often standardize numeric variables and create new variables by manipulating the provided data.<\/div>\n
      \n
      \n
      [T, augTrainData] = genrfeatures(cleanTrainData, “metastatic_diagnosis_period”<\/span>, 30)<\/span><\/div>\n
      \n
      \n
      \n
      T = <\/span><\/div>\n
      \n

      FeatureTransformer with properties:<\/p>\n

      Type: ‘regression’
      \nTargetLearner: ‘linear’
      \nNumEngineeredFeatures: 28
      \nNumOriginalFeatures: 2
      \nTotalNumFeatures: 30<\/p>\n<\/div>\n<\/div>\n<\/div>\n

      \n
      \n
      augTrainData = 12844\u00d731 table <\/span><\/div>\n
      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      <\/th>\nbreast_cancer_diagnosis_code<\/th>\nbreast_cancer_diagnosis_desc<\/th>\nzsc(cos(yearsFromMeanAge))<\/th>\nzsc(health_uninsured.*yearsFromMeanAge)<\/th>\nzsc(AverageOfJan_14-AverageOfFeb_14)<\/th>\nzsc(AverageOfOct_16.\/AverageOfApr_17)<\/th>\nzsc(AverageOfJan_13.\/AverageOfDec_16)<\/th>\neb11(patient_age)<\/th>\neb11(yearsFromMeanAge)<\/th>\nzsc(sin(AverageOfNov_18))<\/th>\nzsc(labor_force_participation+disabled)<\/th>\nzsc(cos(AverageOfJun_13))<\/th>\nzsc(sin(AverageOfOct_18))<\/th>\nzsc(patient_age.\/hispanic)<\/th>\nzsc(sin(age_20s))<\/th>\nzsc(cos(AverageOfJul_15))<\/th>\nzsc(yearsFromMeanAge.^2)<\/th>\nzsc(farmer.*yearsFromMeanAge)<\/th>\nzsc(sig(patient_age))<\/th>\neb24(income_household_100_to_150)<\/th>\nzsc(cos(AverageOfDec_17))<\/th>\nzsc(cos(rent_median))<\/th>\nzsc(tanh(age_40s))<\/th>\nzsc(race_black.*race_pacific)<\/th>\neb28(education_graduate)<\/th>\nzsc(cos(AverageOfAug_18))<\/th>\nzsc(AverageOfMar_13.\/AverageOfFeb_16)<\/th>\nzsc(sin(AverageOfNov_13))<\/th>\nzsc(cos(AverageOfNov_18))<\/th>\nzsc(health_uninsured.\/yearsFromMeanAge)<\/th>\n<\/tr>\n<\/thead>\n
      1<\/th>\nC50912<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\n0.1806<\/td>\n-1.2893<\/td>\n0.0690<\/td>\n-0.3920<\/td>\n0.0890<\/td>\n2<\/td>\n2<\/td>\n-1.2710<\/td>\n0.1981<\/td>\n1.0835<\/td>\n-0.4009<\/td>\n0.2163<\/td>\n-1.1510<\/td>\n1.2808<\/td>\n0.9764<\/td>\n-2.2388<\/td>\n0.0280<\/td>\n1<\/td>\n0.6620<\/td>\n1.1890<\/td>\n0.0776<\/td>\n-0.4848<\/td>\n1<\/td>\n-1.2617<\/td>\n-0.5500<\/td>\n1.3750<\/td>\n0.9595<\/td>\n0.0631<\/td>\n<\/tr>\n
      2<\/th>\nC50412<\/td>\nMalig\u00a0neoplasm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\n-0.6655<\/td>\n-0.2150<\/td>\n0.2483<\/td>\n-0.0630<\/td>\n0.3927<\/td>\n5<\/td>\n5<\/td>\n1.2637<\/td>\n-0.6535<\/td>\n0.0614<\/td>\n1.3875<\/td>\n0.6865<\/td>\n-1.0175<\/td>\n-1.3784<\/td>\n-0.6453<\/td>\n-0.4617<\/td>\n0.0280<\/td>\n4<\/td>\n-1.0882<\/td>\n-1.0191<\/td>\n0.0684<\/td>\n-0.3514<\/td>\n3<\/td>\n1.5636<\/td>\n-0.2443<\/td>\n-1.2160<\/td>\n-0.3726<\/td>\n-0.0954<\/td>\n<\/tr>\n
      3<\/th>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\n1.3154<\/td>\n0.0054<\/td>\n0.5232<\/td>\n0.1254<\/td>\n-0.1329<\/td>\n6<\/td>\n6<\/td>\n-0.2890<\/td>\n-0.7538<\/td>\n-0.7796<\/td>\n1.4197<\/td>\n-0.5775<\/td>\n1.4565<\/td>\n1.0324<\/td>\n-0.7200<\/td>\n-0.0024<\/td>\n0.0280<\/td>\n20<\/td>\n-1.2077<\/td>\n-0.6610<\/td>\n0.0780<\/td>\n0.6752<\/td>\n6<\/td>\n-0.9565<\/td>\n-0.2047<\/td>\n-0.9773<\/td>\n1.5936<\/td>\n-3.9151<\/td>\n<\/tr>\n
      4<\/th>\nC50911<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\n1.3154<\/td>\n6.9626e-04<\/td>\n1.5665<\/td>\n0.4103<\/td>\n0.3231<\/td>\n6<\/td>\n6<\/td>\n0.6535<\/td>\n0.3172<\/td>\n0.9866<\/td>\n-0.2370<\/td>\n-0.5749<\/td>\n-1.1616<\/td>\n-1.3856<\/td>\n-0.7200<\/td>\n-8.3799e-05<\/td>\n0.0280<\/td>\n9<\/td>\n-1.0435<\/td>\n0.2634<\/td>\n0.0790<\/td>\n0.3610<\/td>\n13<\/td>\n1.6817<\/td>\n-1.8093<\/td>\n1.1615<\/td>\n-1.0371<\/td>\n-5.0481<\/td>\n<\/tr>\n
      5<\/th>\n1748<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0other\u00a0specified\u00a0sites\u00a0of\u00a0female\u00a0breast<\/td>\n0.9097<\/td>\n0.6396<\/td>\n1.2400<\/td>\n0.3110<\/td>\n0.0649<\/td>\n9<\/td>\n9<\/td>\n-1.2927<\/td>\n-2.1295<\/td>\n-0.1996<\/td>\n0.8063<\/td>\n-0.5212<\/td>\n0.8644<\/td>\n0.3204<\/td>\n-0.1503<\/td>\n0.3570<\/td>\n0.0280<\/td>\n17<\/td>\n-0.9154<\/td>\n-0.3898<\/td>\n-0.0585<\/td>\n-0.0649<\/td>\n11<\/td>\n0.9956<\/td>\n-1.1728<\/td>\n1.4639<\/td>\n-0.5341<\/td>\n0.1862<\/td>\n<\/tr>\n
      6<\/th>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\n-1.2099<\/td>\n0.1862<\/td>\n5.6886e-04<\/td>\n-0.5225<\/td>\n0.1049<\/td>\n7<\/td>\n7<\/td>\n-0.8391<\/td>\n0.7661<\/td>\n-0.8233<\/td>\n-1.2811<\/td>\n1.4600<\/td>\n-0.9590<\/td>\n-0.9225<\/td>\n-0.6624<\/td>\n0.3039<\/td>\n0.0280<\/td>\n21<\/td>\n-0.3768<\/td>\n-1.0583<\/td>\n0.0783<\/td>\n-0.5007<\/td>\n4<\/td>\n1.2107<\/td>\n0.1415<\/td>\n1.4069<\/td>\n1.3855<\/td>\n0.3022<\/td>\n<\/tr>\n
      7<\/th>\nC50412<\/td>\nMalig\u00a0neoplasm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\n-0.9412<\/td>\n-0.1142<\/td>\n0.0602<\/td>\n-0.3997<\/td>\n0.1448<\/td>\n6<\/td>\n6<\/td>\n-1.4337<\/td>\n0.4192<\/td>\n1.2975<\/td>\n-0.1379<\/td>\n0.4657<\/td>\n-1.1378<\/td>\n-0.3066<\/td>\n-0.6992<\/td>\n-0.2427<\/td>\n0.0280<\/td>\n13<\/td>\n-0.8261<\/td>\n-1.3789<\/td>\n0.0790<\/td>\n-0.4774<\/td>\n1<\/td>\n0.3635<\/td>\n0.2951<\/td>\n0.3963<\/td>\n0.6159<\/td>\n-0.3145<\/td>\n<\/tr>\n
      8<\/th>\nC50411<\/td>\nMalig\u00a0neoplm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\n-1.4468<\/td>\n-0.0944<\/td>\n-0.3012<\/td>\n-1.2779<\/td>\n-0.1211<\/td>\n6<\/td>\n6<\/td>\n-0.1400<\/td>\n0.6606<\/td>\n-1.2905<\/td>\n0.1472<\/td>\n-0.1780<\/td>\n-0.3494<\/td>\n0.1091<\/td>\n-0.6764<\/td>\n-4.9683e-05<\/td>\n0.0280<\/td>\n14<\/td>\n-1.1083<\/td>\n-1.2292<\/td>\n0.0734<\/td>\n-0.3837<\/td>\n20<\/td>\n1.2717<\/td>\n1.6932<\/td>\n0.2868<\/td>\n-1.2188<\/td>\n-0.0616<\/td>\n<\/tr>\n
      9<\/th>\nC50212<\/td>\nMalig\u00a0neoplasm\u00a0of\u00a0upper-inner\u00a0quadrant\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\n1.1607<\/td>\n0.3906<\/td>\n-1.0540<\/td>\n-0.6968<\/td>\n-0.2237<\/td>\n7<\/td>\n8<\/td>\n-0.4944<\/td>\n-0.7009<\/td>\n1.2929<\/td>\n0.9208<\/td>\n1.0272<\/td>\n-1.1630<\/td>\n-0.6645<\/td>\n-0.5839<\/td>\n0.5773<\/td>\n0.0280<\/td>\n9<\/td>\n0.8457<\/td>\n1.2404<\/td>\n0.0737<\/td>\n1.5897<\/td>\n1<\/td>\n1.2651<\/td>\n-0.1321<\/td>\n-0.8378<\/td>\n1.5453<\/td>\n0.2936<\/td>\n<\/tr>\n
      10<\/th>\nC50912<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast<\/td>\n0.9918<\/td>\n0.0709<\/td>\n-0.2100<\/td>\n0.4588<\/td>\n-0.4328<\/td>\n6<\/td>\n6<\/td>\n-1.1849<\/td>\n-1.6220<\/td>\n-1.0465<\/td>\n1.4286<\/td>\n-0.5794<\/td>\n1.5920<\/td>\n0.4326<\/td>\n-0.7180<\/td>\n0.0020<\/td>\n0.0280<\/td>\n4<\/td>\n1.3263<\/td>\n0.4417<\/td>\n-0.8275<\/td>\n-0.1101<\/td>\n4<\/td>\n-0.1867<\/td>\n0.4845<\/td>\n-1.2424<\/td>\n-0.6868<\/td>\n1.5358<\/td>\n<\/tr>\n
      11<\/th>\nC50911<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\n0.9097<\/td>\n0.6159<\/td>\n-0.2138<\/td>\n-1.4477<\/td>\n-0.7481<\/td>\n9<\/td>\n9<\/td>\n-0.8974<\/td>\n1.3344<\/td>\n-1.2279<\/td>\n-0.0286<\/td>\n-0.2587<\/td>\n-0.6343<\/td>\n0.9810<\/td>\n-0.1503<\/td>\n-4.9683e-05<\/td>\n0.0280<\/td>\n7<\/td>\n-1.2504<\/td>\n1.3439<\/td>\n0.0732<\/td>\n0.1566<\/td>\n17<\/td>\n-0.6010<\/td>\n1.1877<\/td>\n-0.2407<\/td>\n-0.9555<\/td>\n0.1834<\/td>\n<\/tr>\n
      12<\/th>\nC50811<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0ovrlp\u00a0sites\u00a0of\u00a0right\u00a0female\u00a0breast<\/td>\n0.4955<\/td>\n0.5783<\/td>\n-0.3673<\/td>\n-0.4973<\/td>\n0.3141<\/td>\n9<\/td>\n9<\/td>\n-1.2544<\/td>\n0.7968<\/td>\n1.1712<\/td>\n-1.4259<\/td>\n1.4166<\/td>\n-0.1312<\/td>\n-0.4201<\/td>\n0.0604<\/td>\n1.0940<\/td>\n0.0280<\/td>\n17<\/td>\n-1.2310<\/td>\n-0.8610<\/td>\n0.0766<\/td>\n-0.4922<\/td>\n5<\/td>\n0.5016<\/td>\n0.1765<\/td>\n1.2579<\/td>\n0.9843<\/td>\n0.1616<\/td>\n<\/tr>\n
      13<\/th>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\n0.4955<\/td>\n0.4398<\/td>\n0.5914<\/td>\n0.1312<\/td>\n-1.7375<\/td>\n9<\/td>\n9<\/td>\n0.8544<\/td>\n1.4799<\/td>\n-1.2113<\/td>\n-0.9255<\/td>\n0.5154<\/td>\n-0.5829<\/td>\n-0.2644<\/td>\n0.0604<\/td>\n1.1872<\/td>\n0.0280<\/td>\n24<\/td>\n-0.3583<\/td>\n0.8771<\/td>\n0.0789<\/td>\n-0.5004<\/td>\n8<\/td>\n1.0030<\/td>\n0.6482<\/td>\n-1.0818<\/td>\n1.2931<\/td>\n0.1498<\/td>\n<\/tr>\n
      14<\/th>\n1749<\/td>\nMalignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified<\/td>\n1.2957<\/td>\n-0.4079<\/td>\n-0.3742<\/td>\n-0.0806<\/td>\n-0.1207<\/td>\n4<\/td>\n4<\/td>\n0.5078<\/td>\n-0.2391<\/td>\n0.3034<\/td>\n-0.6520<\/td>\n0.0119<\/td>\n0.4380<\/td>\n0.9356<\/td>\n-0.0990<\/td>\n-0.1350<\/td>\n0.0280<\/td>\n16<\/td>\n1.3104<\/td>\n-1.4723<\/td>\n0.0770<\/td>\n-0.4757<\/td>\n13<\/td>\n-1.2384<\/td>\n0.6673<\/td>\n-1.2145<\/td>\n-1.1079<\/td>\n0.0685<\/td>\n<\/tr>\n
      \u22ee<\/th>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
      <\/div>\n
      <\/div>\n
      <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
      To better understand the generated features, you can use the describe<\/span><\/a> function of the returned FeatureTransformer<\/span><\/a> object, \u2018T\u2019.<\/div>\n
      \n
      \n
      describe(T)<\/span><\/div>\n
      \n
      \n
      Type<\/strong> IsOriginal<\/strong> InputVariables<\/strong> Transformations<\/strong>
      \n___________<\/strong> __________<\/strong> ___________________________________<\/strong> ______________________________________________________________<\/strong>breast_cancer_diagnosis_code <\/strong> Categorical true breast_cancer_diagnosis_code
      \nbreast_cancer_diagnosis_desc <\/strong> Categorical true breast_cancer_diagnosis_desc
      \nzsc(cos(yearsFromMeanAge)) <\/strong> Numeric false yearsFromMeanAge cos( )
      \nStandardization with z-score (mean = 0.03342, std = 0.70961)
      \nzsc(health_uninsured.*yearsFromMeanAge)<\/strong> Numeric false health_uninsured, yearsFromMeanAge health_uninsured .* yearsFromMeanAge
      \nStandardization with z-score (mean = -2.7558, std = 124.453)
      \nzsc(AverageOfJan_14-AverageOfFeb_14) <\/strong> Numeric false AverageOfJan_14, AverageOfFeb_14 AverageOfJan_14 – AverageOfFeb_14
      \nStandardization with z-score (mean = -2.4227, std = 3.8007)
      \nzsc(AverageOfOct_16.\/AverageOfApr_17) <\/strong> Numeric false AverageOfOct_16, AverageOfApr_17 AverageOfOct_16 .\/ AverageOfApr_17
      \nStandardization with z-score (mean = 1.0531, std = 0.040559)
      \nzsc(AverageOfJan_13.\/AverageOfDec_16) <\/strong> Numeric false AverageOfJan_13, AverageOfDec_16 AverageOfJan_13 .\/ AverageOfDec_16
      \nStandardization with z-score (mean = 0.96755, std = 0.07866)
      \neb11(patient_age) <\/strong> Categorical false patient_age Equal-width binning (number of bins = 11)
      \neb11(yearsFromMeanAge) <\/strong> Categorical false yearsFromMeanAge Equal-width binning (number of bins = 11)
      \nzsc(sin(AverageOfNov_18)) <\/strong> Numeric false AverageOfNov_18 sin( )
      \nStandardization with z-score (mean = 0.039513, std = 0.69365)
      \nzsc(labor_force_participation+disabled)<\/strong> Numeric false labor_force_participation, disabled labor_force_participation + disabled
      \nStandardization with z-score (mean = 75.1061, std = 3.7296)
      \nzsc(cos(AverageOfJun_13)) <\/strong> Numeric false AverageOfJun_13 cos( )
      \nStandardization with z-score (mean = 0.014056, std = 0.75911)
      \nzsc(sin(AverageOfOct_18)) <\/strong> Numeric false AverageOfOct_18 sin( )
      \nStandardization with z-score (mean = -0.00117, std = 0.70011)
      \nzsc(patient_age.\/hispanic) <\/strong> Numeric false patient_age, hispanic patient_age .\/ hispanic
      \nStandardization with z-score (mean = 9.7121, std = 14.6393)
      \nzsc(sin(age_20s)) <\/strong> Numeric false age_20s sin( )
      \nStandardization with z-score (mean = -0.20048, std = 0.68741)
      \nzsc(cos(AverageOfJul_15)) <\/strong> Numeric false AverageOfJul_15 cos( )
      \nStandardization with z-score (mean = 0.012229, std = 0.72983)
      \nzsc(yearsFromMeanAge.^2) <\/strong> Numeric false yearsFromMeanAge power( ,2)
      \nStandardization with z-score (mean = 174.2181, std = 241.8873)
      \nzsc(farmer.*yearsFromMeanAge) <\/strong> Numeric false farmer, yearsFromMeanAge farmer .* yearsFromMeanAge
      \nStandardization with z-score (mean = 0.0023864, std = 48.0329)
      \nzsc(sig(patient_age)) <\/strong> Numeric false patient_age sigmoid( )
      \nStandardization with z-score (mean = 1, std = 2.6634e-10)
      \neb24(income_household_100_to_150) <\/strong> Categorical false income_household_100_to_150 Equal-width binning (number of bins = 24)
      \nzsc(cos(AverageOfDec_17)) <\/strong> Numeric false AverageOfDec_17 cos( )
      \nStandardization with z-score (mean = -0.0045992, std = 0.7565)
      \nzsc(cos(rent_median)) <\/strong> Numeric false rent_median cos( )
      \nStandardization with z-score (mean = 0.053355, std = 0.69262)
      \nzsc(tanh(age_40s)) <\/strong> Numeric false age_40s tanh( )
      \nStandardization with z-score (mean = 1, std = 2.5149e-08)
      \nzsc(race_black.*race_pacific) <\/strong> Numeric false race_black, race_pacific race_black .* race_pacific
      \nStandardization with z-score (mean = 1.0419, std = 2.0598)
      \neb28(education_graduate) <\/strong> Categorical false education_graduate Equal-width binning (number of bins = 28)
      \nzsc(cos(AverageOfAug_18)) <\/strong> Numeric false AverageOfAug_18 cos( )
      \nStandardization with z-score (mean = -0.13184, std = 0.66549)
      \nzsc(AverageOfMar_13.\/AverageOfFeb_16) <\/strong> Numeric false AverageOfMar_13, AverageOfFeb_16 AverageOfMar_13 .\/ AverageOfFeb_16
      \nStandardization with z-score (mean = 1.0327, std = 0.065144)
      \nzsc(sin(AverageOfNov_13)) <\/strong> Numeric false AverageOfNov_13 sin( )
      \nStandardization with z-score (mean = -0.075478, std = 0.73244)
      \nzsc(cos(AverageOfNov_18)) <\/strong> Numeric false AverageOfNov_18 cos( )
      \nStandardization with z-score (mean = -0.13799, std = 0.70592)
      \nzsc(health_uninsured.\/yearsFromMeanAge)<\/strong> Numeric false health_uninsured, yearsFromMeanAge health_uninsured .\/ yearsFromMeanAge
      \nStandardization with z-score (mean = -0.88776, std = 7.7614)<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

      Train a Machine Learning Model<\/h2>\n
      In this example, I’ll use the fitrauto<\/span><\/a> function to automatically test a variety of regression model types and hyperparameter values and select the best one. I use ASHA optimization, as it tends to find good solutions quickly for data sets with many observations, and I choose to use holdout validation, which uses 20% of the dataset for testing. You should play around with these values to see what improvements you can make.<\/div>\n
      \n
      \n
      hypParamOptions.Optimizer = “asha”<\/span>;<\/span><\/div>\n<\/div>\n
      \n
      hypParamOptions.Holdout = 0.2;<\/span><\/div>\n<\/div>\n
      \n
      Mdl = fitrauto(augTrainData, “metastatic_diagnosis_period”<\/span>, “HyperparameterOptimizationOptions”<\/span>, hypParamOptions)<\/span><\/div>\n
      \n
      \n
      Learner types to explore: ensemble, svm, tree
      \nTotal iterations (MaxObjectiveEvaluations): 255
      \nTotal time (MaxTime): Inf|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 1 | Best | 9.3862 | 1.0312 | 9.3862 | 161 | tree | MinLeafSize: 173 |
      \n| 2 | Best | 9.384 | 2.092 | 9.384 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 232 |
      \n| | | | | | | | MinLeafSize: 1084 |
      \n| 3 | Accept | 38.104 | 0.45806 | 9.384 | 161 | svm | BoxConstraint: 0.011812 |
      \n| | | | | | | | KernelScale: 2.2883 |
      \n| | | | | | | | Epsilon: 38.982 |
      \n| 4 | Best | 9.3825 | 0.28987 | 9.3825 | 161 | tree | MinLeafSize: 140 |
      \n| 5 | Best | 8.9046 | 0.21544 | 8.9046 | 643 | tree | MinLeafSize: 140 |
      \n| 6 | Accept | 9.3853 | 0.10889 | 8.9046 | 161 | tree | MinLeafSize: 5183 |
      \n| 7 | Accept | 8.9281 | 0.10868 | 8.9046 | 161 | tree | MinLeafSize: 45 |
      \n| 8 | Accept | 63.931 | 0.48431 | 8.9046 | 161 | svm | BoxConstraint: 0.0032309 |
      \n| | | | | | | | KernelScale: 4.7109 |
      \n| | | | | | | | Epsilon: 8.986 |
      \n| 9 | Accept | 45.964 | 4.3819 | 8.9046 | 161 | svm | BoxConstraint: 0.12087 |
      \n| | | | | | | | KernelScale: 0.088521 |
      \n| | | | | | | | Epsilon: 0.97865 |
      \n| 10 | Accept | 8.94 | 0.067605 | 8.9046 | 643 | tree | MinLeafSize: 45 |
      \n| 11 | Accept | 71.49 | 4.8778 | 8.9046 | 161 | svm | BoxConstraint: 317.32 |
      \n| | | | | | | | KernelScale: 0.010993 |
      \n| | | | | | | | Epsilon: 19.065 |
      \n| 12 | Accept | 9.3893 | 0.16924 | 8.9046 | 161 | svm | BoxConstraint: 0.11231 |
      \n| | | | | | | | KernelScale: 34.956 |
      \n| | | | | | | | Epsilon: 4417.5 |
      \n| 13 | Accept | 9.3273 | 0.051708 | 8.9046 | 161 | tree | MinLeafSize: 33 |
      \n| 14 | Accept | 9.4163 | 0.061619 | 8.9046 | 161 | svm | BoxConstraint: 0.12262 |
      \n| | | | | | | | KernelScale: 16.877 |
      \n| | | | | | | | Epsilon: 539.11 |
      \n| 15 | Accept | 8.9338 | 0.066245 | 8.9046 | 643 | tree | MinLeafSize: 33 |
      \n| 16 | Accept | 43.881 | 0.082721 | 8.9046 | 161 | svm | BoxConstraint: 49.319 |
      \n| | | | | | | | KernelScale: 4.6223 |
      \n| | | | | | | | Epsilon: 59.775 |
      \n| 17 | Accept | 9.4191 | 0.057352 | 8.9046 | 161 | svm | BoxConstraint: 0.16688 |
      \n| | | | | | | | KernelScale: 0.0023583 |
      \n| | | | | | | | Epsilon: 1744.5 |
      \n| 18 | Accept | 9.393 | 0.058098 | 8.9046 | 161 | svm | BoxConstraint: 0.17661 |
      \n| | | | | | | | KernelScale: 0.0014019 |
      \n| | | | | | | | Epsilon: 872.82 |
      \n| 19 | Accept | 9.2595 | 0.058433 | 8.9046 | 161 | tree | MinLeafSize: 3 |
      \n| 20 | Accept | 9.3043 | 0.085948 | 8.9046 | 643 | tree | MinLeafSize: 3 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 21 | Best | 8.8832 | 0.080987 | 8.8832 | 2569 | tree | MinLeafSize: 140 |
      \n| 22 | Accept | 9.4095 | 0.061143 | 8.8832 | 161 | svm | BoxConstraint: 0.018037 |
      \n| | | | | | | | KernelScale: 61.209 |
      \n| | | | | | | | Epsilon: 256.27 |
      \n| 23 | Accept | 9.4198 | 0.057961 | 8.8832 | 161 | svm | BoxConstraint: 0.11446 |
      \n| | | | | | | | KernelScale: 15.272 |
      \n| | | | | | | | Epsilon: 197.66 |
      \n| 24 | Accept | 9.1218 | 0.050487 | 8.8832 | 161 | tree | MinLeafSize: 34 |
      \n| 25 | Accept | 9.3875 | 1.7749 | 8.8832 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 294 |
      \n| | | | | | | | MinLeafSize: 273 |
      \n| 26 | Accept | 8.9284 | 0.11047 | 8.8832 | 643 | tree | MinLeafSize: 34 |
      \n| 27 | Accept | 9.3863 | 0.052435 | 8.8832 | 161 | tree | MinLeafSize: 2719 |
      \n| 28 | Accept | 9.4099 | 0.065684 | 8.8832 | 161 | svm | BoxConstraint: 0.011394 |
      \n| | | | | | | | KernelScale: 0.0018703 |
      \n| | | | | | | | Epsilon: 3.3641 |
      \n| 29 | Accept | 12.819 | 0.5708 | 8.8832 | 161 | svm | BoxConstraint: 28.941 |
      \n| | | | | | | | KernelScale: 6.0836 |
      \n| | | | | | | | Epsilon: 31.22 |
      \n| 30 | Accept | 67.346 | 0.58923 | 8.8832 | 161 | svm | BoxConstraint: 244.94 |
      \n| | | | | | | | KernelScale: 8.5597 |
      \n| | | | | | | | Epsilon: 2.3973 |
      \n| 31 | Accept | 9.3879 | 1.4179 | 8.8832 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 232 |
      \n| | | | | | | | MinLeafSize: 1084 |
      \n| 32 | Accept | 65.415 | 0.091055 | 8.8832 | 161 | svm | BoxConstraint: 1.2417 |
      \n| | | | | | | | KernelScale: 0.0050643 |
      \n| | | | | | | | Epsilon: 76.681 |
      \n| 33 | Accept | 9.4225 | 0.060363 | 8.8832 | 161 | svm | BoxConstraint: 0.0084308 |
      \n| | | | | | | | KernelScale: 833.3 |
      \n| | | | | | | | Epsilon: 730.67 |
      \n| 34 | Accept | 8.9623 | 2.1224 | 8.8832 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 285 |
      \n| | | | | | | | MinLeafSize: 12 |
      \n| 35 | Accept | 50.056 | 0.56976 | 8.8832 | 161 | svm | BoxConstraint: 0.72025 |
      \n| | | | | | | | KernelScale: 9.8778 |
      \n| | | | | | | | Epsilon: 6.4438 |
      \n| 36 | Best | 8.8771 | 2.5786 | 8.8771 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 285 |
      \n| | | | | | | | MinLeafSize: 12 |
      \n| 37 | Accept | 9.0976 | 0.084623 | 8.8771 | 161 | tree | MinLeafSize: 8 |
      \n| 38 | Accept | 9.3819 | 1.4843 | 8.8771 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 294 |
      \n| | | | | | | | MinLeafSize: 1255 |
      \n| 39 | Accept | 8.9822 | 1.5021 | 8.8771 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 208 |
      \n| | | | | | | | MinLeafSize: 16 |
      \n| 40 | Accept | 45.349 | 0.50465 | 8.8771 | 161 | svm | BoxConstraint: 0.0031861 |
      \n| | | | | | | | KernelScale: 1.1929 |
      \n| | | | | | | | Epsilon: 3.846 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 41 | Best | 8.8726 | 1.7837 | 8.8726 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 208 |
      \n| | | | | | | | MinLeafSize: 16 |
      \n| 42 | Best | 8.8377 | 2.8179 | 8.8377 | 2569 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 208 |
      \n| | | | | | | | MinLeafSize: 16 |
      \n| 43 | Accept | 9.3834 | 1.4848 | 8.8377 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 269 |
      \n| | | | | | | | MinLeafSize: 101 |
      \n| 44 | Accept | 9.4026 | 0.075617 | 8.8377 | 161 | svm | BoxConstraint: 120.1 |
      \n| | | | | | | | KernelScale: 1.5209 |
      \n| | | | | | | | Epsilon: 1610.5 |
      \n| 45 | Accept | 8.9233 | 1.5883 | 8.8377 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 224 |
      \n| | | | | | | | MinLeafSize: 4 |
      \n| 46 | Accept | 34.178 | 4.802 | 8.8377 | 161 | svm | BoxConstraint: 663.55 |
      \n| | | | | | | | KernelScale: 0.045175 |
      \n| | | | | | | | Epsilon: 3.6348 |
      \n| 47 | Accept | 8.8728 | 1.9803 | 8.8377 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 224 |
      \n| | | | | | | | MinLeafSize: 4 |
      \n| 48 | Accept | 16.005 | 2.1964 | 8.8377 | 161 | svm | BoxConstraint: 41.536 |
      \n| | | | | | | | KernelScale: 0.13288 |
      \n| | | | | | | | Epsilon: 0.76209 |
      \n| 49 | Accept | 9.5967 | 5.0388 | 8.8377 | 161 | svm | BoxConstraint: 434.82 |
      \n| | | | | | | | KernelScale: 0.31522 |
      \n| | | | | | | | Epsilon: 5.0709 |
      \n| 50 | Accept | 9.4046 | 0.056141 | 8.8377 | 161 | svm | BoxConstraint: 0.0019764 |
      \n| | | | | | | | KernelScale: 0.98483 |
      \n| | | | | | | | Epsilon: 304.63 |
      \n| 51 | Accept | 35.523 | 4.2078 | 8.8377 | 161 | svm | BoxConstraint: 0.017662 |
      \n| | | | | | | | KernelScale: 0.0065272 |
      \n| | | | | | | | Epsilon: 1.7329 |
      \n| 52 | Accept | 9.1215 | 0.11814 | 8.8377 | 643 | tree | MinLeafSize: 8 |
      \n| 53 | Accept | 9.3878 | 1.4928 | 8.8377 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 264 |
      \n| | | | | | | | MinLeafSize: 2956 |
      \n| 54 | Accept | 9.3846 | 0.054412 | 8.8377 | 161 | tree | MinLeafSize: 125 |
      \n| 55 | Accept | 9.0298 | 0.053416 | 8.8377 | 161 | tree | MinLeafSize: 34 |
      \n| 56 | Accept | 9.4066 | 0.067662 | 8.8377 | 161 | svm | BoxConstraint: 0.21675 |
      \n| | | | | | | | KernelScale: 79.17 |
      \n| | | | | | | | Epsilon: 764.21 |
      \n| 57 | Accept | 8.942 | 0.07282 | 8.8377 | 643 | tree | MinLeafSize: 34 |
      \n| 58 | Accept | 9.3883 | 1.3873 | 8.8377 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 267 |
      \n| | | | | | | | MinLeafSize: 3998 |
      \n| 59 | Accept | 9.1155 | 1.9223 | 8.8377 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 255 |
      \n| | | | | | | | MinLeafSize: 7 |
      \n| 60 | Accept | 11.03 | 0.093069 | 8.8377 | 161 | svm | BoxConstraint: 0.34881 |
      \n| | | | | | | | KernelScale: 1.0691 |
      \n| | | | | | | | Epsilon: 61.589 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 61 | Accept | 20.181 | 0.61277 | 8.8377 | 161 | svm | BoxConstraint: 82.516 |
      \n| | | | | | | | KernelScale: 9.0767 |
      \n| | | | | | | | Epsilon: 1.705 |
      \n| 62 | Accept | 8.98 | 3.2622 | 8.8377 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 255 |
      \n| | | | | | | | MinLeafSize: 7 |
      \n| 63 | Accept | 8.8565 | 3.0996 | 8.8377 | 2569 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 224 |
      \n| | | | | | | | MinLeafSize: 4 |
      \n| 64 | Accept | 9.3866 | 1.0202 | 8.8377 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 207 |
      \n| | | | | | | | MinLeafSize: 6286 |
      \n| 65 | Accept | 31.849 | 0.56723 | 8.8377 | 161 | svm | BoxConstraint: 164.7 |
      \n| | | | | | | | KernelScale: 977.44 |
      \n| | | | | | | | Epsilon: 4.0873 |
      \n| 66 | Accept | 9.3456 | 0.08663 | 8.8377 | 161 | tree | MinLeafSize: 3 |
      \n| 67 | Accept | 9.3898 | 0.046713 | 8.8377 | 161 | tree | MinLeafSize: 568 |
      \n| 68 | Accept | 9.3241 | 0.089116 | 8.8377 | 643 | tree | MinLeafSize: 3 |
      \n| 69 | Accept | 25.765 | 0.62032 | 8.8377 | 161 | svm | BoxConstraint: 0.12804 |
      \n| | | | | | | | KernelScale: 2.8982 |
      \n| | | | | | | | Epsilon: 0.26435 |
      \n| 70 | Accept | 9.392 | 0.061827 | 8.8377 | 161 | svm | BoxConstraint: 0.0036781 |
      \n| | | | | | | | KernelScale: 135.24 |
      \n| | | | | | | | Epsilon: 10235 |
      \n| 71 | Accept | 65.542 | 4.5555 | 8.8377 | 161 | svm | BoxConstraint: 429.64 |
      \n| | | | | | | | KernelScale: 0.1032 |
      \n| | | | | | | | Epsilon: 140.83 |
      \n| 72 | Accept | 9.6253 | 2.2298 | 8.8377 | 161 | svm | BoxConstraint: 28.772 |
      \n| | | | | | | | KernelScale: 0.1677 |
      \n| | | | | | | | Epsilon: 0.1355 |
      \n| 73 | Accept | 9.3874 | 1.5589 | 8.8377 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 294 |
      \n| | | | | | | | MinLeafSize: 1255 |
      \n| 74 | Accept | 51.726 | 4.4621 | 8.8377 | 161 | svm | BoxConstraint: 210.52 |
      \n| | | | | | | | KernelScale: 0.03399 |
      \n| | | | | | | | Epsilon: 143.6 |
      \n| 75 | Accept | 8.9997 | 2.0089 | 8.8377 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 281 |
      \n| | | | | | | | MinLeafSize: 4 |
      \n| 76 | Accept | 9.4831 | 4.4187 | 8.8377 | 161 | svm | BoxConstraint: 12.41 |
      \n| | | | | | | | KernelScale: 0.046831 |
      \n| | | | | | | | Epsilon: 0.18991 |
      \n| 77 | Accept | 9.2437 | 0.063799 | 8.8377 | 161 | tree | MinLeafSize: 65 |
      \n| 78 | Accept | 8.8737 | 2.5522 | 8.8377 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 281 |
      \n| | | | | | | | MinLeafSize: 4 |
      \n| 79 | Accept | 9.3952 | 1.5512 | 8.8377 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 274 |
      \n| | | | | | | | MinLeafSize: 2137 |
      \n| 80 | Accept | 9.3839 | 1.2513 | 8.8377 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 225 |
      \n| | | | | | | | MinLeafSize: 4427 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 81 | Accept | 9.3187 | 0.065288 | 8.8377 | 161 | tree | MinLeafSize: 1 |
      \n| 82 | Accept | 73.182 | 0.65686 | 8.8377 | 161 | svm | BoxConstraint: 8.1923 |
      \n| | | | | | | | KernelScale: 49.754 |
      \n| | | | | | | | Epsilon: 26.414 |
      \n| 83 | Accept | 8.9351 | 0.062065 | 8.8377 | 643 | tree | MinLeafSize: 65 |
      \n| 84 | Accept | 8.8466 | 3.9785 | 8.8377 | 2569 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 281 |
      \n| | | | | | | | MinLeafSize: 4 |
      \n| 85 | Best | 8.8186 | 4.6234 | 8.8186 | 10276 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 208 |
      \n| | | | | | | | MinLeafSize: 16 |
      \n| 86 | Accept | 49.608 | 4.8881 | 8.8186 | 161 | svm | BoxConstraint: 0.0038653 |
      \n| | | | | | | | KernelScale: 0.084163 |
      \n| | | | | | | | Epsilon: 0.17521 |
      \n| 87 | Accept | 9.3834 | 0.07619 | 8.8186 | 161 | tree | MinLeafSize: 4107 |
      \n| 88 | Accept | 26.492 | 0.67516 | 8.8186 | 161 | svm | BoxConstraint: 2.5636 |
      \n| | | | | | | | KernelScale: 26.944 |
      \n| | | | | | | | Epsilon: 6.7933 |
      \n| 89 | Accept | 9.3862 | 0.058577 | 8.8186 | 161 | svm | BoxConstraint: 0.0046431 |
      \n| | | | | | | | KernelScale: 0.0018285 |
      \n| | | | | | | | Epsilon: 912.14 |
      \n| 90 | Accept | 9.3978 | 0.1019 | 8.8186 | 643 | tree | MinLeafSize: 1 |
      \n| 91 | Accept | 9.183 | 0.059411 | 8.8186 | 161 | tree | MinLeafSize: 7 |
      \n| 92 | Accept | 9.4018 | 0.065758 | 8.8186 | 161 | svm | BoxConstraint: 0.011254 |
      \n| | | | | | | | KernelScale: 1.6707 |
      \n| | | | | | | | Epsilon: 1282.9 |
      \n| 93 | Accept | 9.4118 | 1.3933 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 246 |
      \n| | | | | | | | MinLeafSize: 5704 |
      \n| 94 | Accept | 50.857 | 4.4175 | 8.8186 | 161 | svm | BoxConstraint: 184.91 |
      \n| | | | | | | | KernelScale: 300 |
      \n| | | | | | | | Epsilon: 9.9176 |
      \n| 95 | Accept | 9.2085 | 0.087503 | 8.8186 | 643 | tree | MinLeafSize: 7 |
      \n| 96 | Accept | 9.4498 | 4.023 | 8.8186 | 161 | svm | BoxConstraint: 0.0021245 |
      \n| | | | | | | | KernelScale: 103.57 |
      \n| | | | | | | | Epsilon: 59.501 |
      \n| 97 | Accept | 9.3829 | 0.0519 | 8.8186 | 161 | tree | MinLeafSize: 225 |
      \n| 98 | Accept | 9.4148 | 0.64092 | 8.8186 | 161 | svm | BoxConstraint: 1.1581 |
      \n| | | | | | | | KernelScale: 375.69 |
      \n| | | | | | | | Epsilon: 1.2079 |
      \n| 99 | Accept | 9.4039 | 0.057192 | 8.8186 | 161 | svm | BoxConstraint: 0.0046686 |
      \n| | | | | | | | KernelScale: 0.0075742 |
      \n| | | | | | | | Epsilon: 6458.5 |
      \n| 100 | Accept | 9.2181 | 0.05773 | 8.8186 | 643 | tree | MinLeafSize: 225 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 101 | Accept | 9.3843 | 1.2718 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 253 |
      \n| | | | | | | | MinLeafSize: 544 |
      \n| 102 | Accept | 9.3807 | 1.3051 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 228 |
      \n| | | | | | | | MinLeafSize: 100 |
      \n| 103 | Accept | 21.2 | 0.66422 | 8.8186 | 161 | svm | BoxConstraint: 0.0091956 |
      \n| | | | | | | | KernelScale: 6.027 |
      \n| | | | | | | | Epsilon: 0.19667 |
      \n| 104 | Accept | 10.175 | 0.079159 | 8.8186 | 161 | svm | BoxConstraint: 0.10113 |
      \n| | | | | | | | KernelScale: 72.6 |
      \n| | | | | | | | Epsilon: 70.924 |
      \n| 105 | Accept | 8.9431 | 3.0941 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 228 |
      \n| | | | | | | | MinLeafSize: 100 |
      \n| 106 | Accept | 8.8463 | 4.1395 | 8.8186 | 2569 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 285 |
      \n| | | | | | | | MinLeafSize: 12 |
      \n| 107 | Accept | 12.042 | 4.2272 | 8.8186 | 161 | svm | BoxConstraint: 0.0062352 |
      \n| | | | | | | | KernelScale: 0.1105 |
      \n| | | | | | | | Epsilon: 0.54085 |
      \n| 108 | Accept | 9.3848 | 1.2747 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 218 |
      \n| | | | | | | | MinLeafSize: 840 |
      \n| 109 | Accept | 9.3934 | 0.058925 | 8.8186 | 161 | svm | BoxConstraint: 5.6969 |
      \n| | | | | | | | KernelScale: 0.023262 |
      \n| | | | | | | | Epsilon: 7846.5 |
      \n| 110 | Accept | 9.0115 | 0.073708 | 8.8186 | 161 | tree | MinLeafSize: 37 |
      \n| 111 | Accept | 8.9938 | 0.060299 | 8.8186 | 643 | tree | MinLeafSize: 37 |
      \n| 112 | Accept | 9.391 | 0.048814 | 8.8186 | 161 | tree | MinLeafSize: 1820 |
      \n| 113 | Accept | 9.3873 | 0.074416 | 8.8186 | 161 | svm | BoxConstraint: 10.972 |
      \n| | | | | | | | KernelScale: 0.0019127 |
      \n| | | | | | | | Epsilon: 2.2406 |
      \n| 114 | Accept | 8.9535 | 1.9056 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 266 |
      \n| | | | | | | | MinLeafSize: 16 |
      \n| 115 | Accept | 9.397 | 1.0038 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 201 |
      \n| | | | | | | | MinLeafSize: 474 |
      \n| 116 | Accept | 8.8859 | 2.4423 | 8.8186 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 266 |
      \n| | | | | | | | MinLeafSize: 16 |
      \n| 117 | Accept | 9.3736 | 0.062757 | 8.8186 | 161 | tree | MinLeafSize: 4 |
      \n| 118 | Accept | 9.3833 | 1.6432 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 286 |
      \n| | | | | | | | MinLeafSize: 415 |
      \n| 119 | Accept | 9.4034 | 0.052013 | 8.8186 | 161 | tree | MinLeafSize: 163 |
      \n| 120 | Accept | 65.505 | 4.21 | 8.8186 | 161 | svm | BoxConstraint: 126.42 |
      \n| | | | | | | | KernelScale: 0.00956 |
      \n| | | | | | | | Epsilon: 0.77659 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 121 | Accept | 9.3102 | 0.098523 | 8.8186 | 643 | tree | MinLeafSize: 4 |
      \n| 122 | Accept | 15.235 | 3.8075 | 8.8186 | 161 | svm | BoxConstraint: 0.042479 |
      \n| | | | | | | | KernelScale: 0.054739 |
      \n| | | | | | | | Epsilon: 126.07 |
      \n| 123 | Accept | 9.3993 | 1.2692 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 220 |
      \n| | | | | | | | MinLeafSize: 305 |
      \n| 124 | Accept | 9.3933 | 0.047521 | 8.8186 | 161 | tree | MinLeafSize: 181 |
      \n| 125 | Accept | 9.4005 | 0.064158 | 8.8186 | 161 | svm | BoxConstraint: 0.75184 |
      \n| | | | | | | | KernelScale: 103.16 |
      \n| | | | | | | | Epsilon: 7561.9 |
      \n| 126 | Accept | 9.3843 | 1.5854 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 286 |
      \n| | | | | | | | MinLeafSize: 415 |
      \n| 127 | Accept | 8.8356 | 3.6654 | 8.8186 | 2569 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 266 |
      \n| | | | | | | | MinLeafSize: 16 |
      \n| 128 | Accept | 9.3867 | 0.063994 | 8.8186 | 161 | tree | MinLeafSize: 5651 |
      \n| 129 | Accept | 13.063 | 2.1824 | 8.8186 | 161 | svm | BoxConstraint: 42.956 |
      \n| | | | | | | | KernelScale: 0.15102 |
      \n| | | | | | | | Epsilon: 150.18 |
      \n| 130 | Accept | 9.6326 | 0.60577 | 8.8186 | 161 | svm | BoxConstraint: 0.001179 |
      \n| | | | | | | | KernelScale: 210.03 |
      \n| | | | | | | | Epsilon: 0.64499 |
      \n| 131 | Accept | 9.3849 | 1.0936 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 207 |
      \n| | | | | | | | MinLeafSize: 3437 |
      \n| 132 | Accept | 9.3808 | 0.0571 | 8.8186 | 643 | tree | MinLeafSize: 4107 |
      \n| 133 | Accept | 36.663 | 0.084854 | 8.8186 | 161 | svm | BoxConstraint: 0.0013618 |
      \n| | | | | | | | KernelScale: 5.0765 |
      \n| | | | | | | | Epsilon: 127.19 |
      \n| 134 | Accept | 16.268 | 3.842 | 8.8186 | 161 | svm | BoxConstraint: 0.0064316 |
      \n| | | | | | | | KernelScale: 0.19009 |
      \n| | | | | | | | Epsilon: 1.1912 |
      \n| 135 | Accept | 9.5749 | 0.6606 | 8.8186 | 161 | svm | BoxConstraint: 0.089516 |
      \n| | | | | | | | KernelScale: 127.63 |
      \n| | | | | | | | Epsilon: 1.7522 |
      \n| 136 | Accept | 9.3917 | 1.1714 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 234 |
      \n| | | | | | | | MinLeafSize: 5148 |
      \n| 137 | Accept | 8.9512 | 2.9923 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 269 |
      \n| | | | | | | | MinLeafSize: 101 |
      \n| 138 | Accept | 9.1052 | 0.0628 | 8.8186 | 161 | tree | MinLeafSize: 7 |
      \n| 139 | Accept | 9.398 | 0.081349 | 8.8186 | 161 | svm | BoxConstraint: 0.016058 |
      \n| | | | | | | | KernelScale: 183.58 |
      \n| | | | | | | | Epsilon: 503.13 |
      \n| 140 | Accept | 9.4164 | 0.056624 | 8.8186 | 161 | tree | MinLeafSize: 1758 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 141 | Accept | 9.4052 | 0.061562 | 8.8186 | 161 | svm | BoxConstraint: 0.023222 |
      \n| | | | | | | | KernelScale: 76.906 |
      \n| | | | | | | | Epsilon: 8814 |
      \n| 142 | Accept | 9.1784 | 0.082115 | 8.8186 | 643 | tree | MinLeafSize: 7 |
      \n| 143 | Accept | 9.2477 | 2.0155 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 272 |
      \n| | | | | | | | MinLeafSize: 1 |
      \n| 144 | Accept | 9.4018 | 0.06487 | 8.8186 | 161 | svm | BoxConstraint: 626.86 |
      \n| | | | | | | | KernelScale: 0.43541 |
      \n| | | | | | | | Epsilon: 1627.4 |
      \n| 145 | Accept | 9.3975 | 0.058275 | 8.8186 | 161 | svm | BoxConstraint: 0.0028588 |
      \n| | | | | | | | KernelScale: 209.66 |
      \n| | | | | | | | Epsilon: 4232.3 |
      \n| 146 | Accept | 9.521 | 0.6356 | 8.8186 | 161 | svm | BoxConstraint: 0.083407 |
      \n| | | | | | | | KernelScale: 312.85 |
      \n| | | | | | | | Epsilon: 0.20668 |
      \n| 147 | Accept | 8.9708 | 3.2479 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 272 |
      \n| | | | | | | | MinLeafSize: 1 |
      \n| 148 | Accept | 8.9616 | 0.10093 | 8.8186 | 2569 | tree | MinLeafSize: 34 |
      \n| 149 | Accept | 15.713 | 4.9592 | 8.8186 | 161 | svm | BoxConstraint: 0.019721 |
      \n| | | | | | | | KernelScale: 0.006631 |
      \n| | | | | | | | Epsilon: 0.81317 |
      \n| 150 | Accept | 61.246 | 2.2315 | 8.8186 | 161 | svm | BoxConstraint: 0.10628 |
      \n| | | | | | | | KernelScale: 0.26584 |
      \n| | | | | | | | Epsilon: 56.177 |
      \n| 151 | Accept | 9.3827 | 1.118 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 214 |
      \n| | | | | | | | MinLeafSize: 314 |
      \n| 152 | Accept | 9.776 | 4.5082 | 8.8186 | 161 | svm | BoxConstraint: 0.0013601 |
      \n| | | | | | | | KernelScale: 0.046336 |
      \n| | | | | | | | Epsilon: 5.0766 |
      \n| 153 | Accept | 9.3125 | 1.2559 | 8.8186 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 214 |
      \n| | | | | | | | MinLeafSize: 314 |
      \n| 154 | Accept | 9.397 | 1.4413 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 273 |
      \n| | | | | | | | MinLeafSize: 594 |
      \n| 155 | Accept | 9.3904 | 0.067118 | 8.8186 | 161 | svm | BoxConstraint: 0.0014004 |
      \n| | | | | | | | KernelScale: 41.954 |
      \n| | | | | | | | Epsilon: 6132.6 |
      \n| 156 | Accept | 11.159 | 0.074313 | 8.8186 | 161 | svm | BoxConstraint: 0.013397 |
      \n| | | | | | | | KernelScale: 9.1715 |
      \n| | | | | | | | Epsilon: 81.019 |
      \n| 157 | Accept | 22.357 | 4.3335 | 8.8186 | 161 | svm | BoxConstraint: 0.41907 |
      \n| | | | | | | | KernelScale: 0.010689 |
      \n| | | | | | | | Epsilon: 13.091 |
      \n| 158 | Accept | 9.3881 | 1.2611 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 225 |
      \n| | | | | | | | MinLeafSize: 4427 |
      \n| 159 | Accept | 9.4028 | 0.067058 | 8.8186 | 161 | svm | BoxConstraint: 0.036022 |
      \n| | | | | | | | KernelScale: 8.618 |
      \n| | | | | | | | Epsilon: 12523 |
      \n| 160 | Accept | 9.5619 | 4.8535 | 8.8186 | 161 | svm | BoxConstraint: 5.6235 |
      \n| | | | | | | | KernelScale: 0.020708 |
      \n| | | | | | | | Epsilon: 0.15719 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 161 | Accept | 9.385 | 0.070467 | 8.8186 | 161 | tree | MinLeafSize: 2083 |
      \n| 162 | Accept | 9.4042 | 0.061121 | 8.8186 | 161 | svm | BoxConstraint: 212.83 |
      \n| | | | | | | | KernelScale: 0.0011315 |
      \n| | | | | | | | Epsilon: 4.8239 |
      \n| 163 | Accept | 9.3832 | 1.3395 | 8.8186 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 253 |
      \n| | | | | | | | MinLeafSize: 544 |
      \n| 164 | Accept | 9.4427 | 0.062918 | 8.8186 | 161 | svm | BoxConstraint: 40.982 |
      \n| | | | | | | | KernelScale: 51.518 |
      \n| | | | | | | | Epsilon: 276.22 |
      \n| 165 | Accept | 9.3838 | 0.052175 | 8.8186 | 161 | tree | MinLeafSize: 259 |
      \n| 166 | Accept | 9.3923 | 0.044845 | 8.8186 | 161 | tree | MinLeafSize: 174 |
      \n| 167 | Accept | 9.3843 | 0.064853 | 8.8186 | 161 | svm | BoxConstraint: 2.4613 |
      \n| | | | | | | | KernelScale: 0.0059067 |
      \n| | | | | | | | Epsilon: 2318.5 |
      \n| 168 | Accept | 9.2331 | 0.058123 | 8.8186 | 643 | tree | MinLeafSize: 259 |
      \n| 169 | Accept | 8.9465 | 0.09373 | 8.8186 | 2569 | tree | MinLeafSize: 33 |
      \n| 170 | Accept | 8.8205 | 5.9209 | 8.8186 | 10276 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 266 |
      \n| | | | | | | | MinLeafSize: 16 |
      \n| 171 | Accept | 9.0308 | 0.055452 | 8.8186 | 161 | tree | MinLeafSize: 25 |
      \n| 172 | Accept | 9.4106 | 0.064019 | 8.8186 | 161 | svm | BoxConstraint: 5.1299 |
      \n| | | | | | | | KernelScale: 0.0049434 |
      \n| | | | | | | | Epsilon: 2964.7 |
      \n| 173 | Accept | 8.9875 | 0.049886 | 8.8186 | 161 | tree | MinLeafSize: 17 |
      \n| 174 | Accept | 9.6815 | 0.068647 | 8.8186 | 161 | svm | BoxConstraint: 0.012521 |
      \n| | | | | | | | KernelScale: 5.8218 |
      \n| | | | | | | | Epsilon: 158.28 |
      \n| 175 | Accept | 9.0889 | 0.080584 | 8.8186 | 643 | tree | MinLeafSize: 17 |
      \n| 176 | Accept | 9.0743 | 0.051929 | 8.8186 | 161 | tree | MinLeafSize: 9 |
      \n| 177 | Accept | 50.143 | 0.53578 | 8.8186 | 161 | svm | BoxConstraint: 0.0025675 |
      \n| | | | | | | | KernelScale: 2.9123 |
      \n| | | | | | | | Epsilon: 2.7823 |
      \n| 178 | Accept | 11.317 | 0.65696 | 8.8186 | 161 | svm | BoxConstraint: 0.0013653 |
      \n| | | | | | | | KernelScale: 0.72963 |
      \n| | | | | | | | Epsilon: 1.9059 |
      \n| 179 | Accept | 8.9881 | 1.9317 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 273 |
      \n| | | | | | | | MinLeafSize: 4 |
      \n| 180 | Accept | 8.8611 | 2.3584 | 8.8186 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 273 |
      \n| | | | | | | | MinLeafSize: 4 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 181 | Accept | 17.128 | 0.082904 | 8.8186 | 161 | svm | BoxConstraint: 882.02 |
      \n| | | | | | | | KernelScale: 3.6447 |
      \n| | | | | | | | Epsilon: 40.81 |
      \n| 182 | Accept | 9.3873 | 0.059449 | 8.8186 | 161 | svm | BoxConstraint: 0.036152 |
      \n| | | | | | | | KernelScale: 128.56 |
      \n| | | | | | | | Epsilon: 676.9 |
      \n| 183 | Accept | 14.295 | 0.59637 | 8.8186 | 161 | svm | BoxConstraint: 0.036148 |
      \n| | | | | | | | KernelScale: 5.6466 |
      \n| | | | | | | | Epsilon: 3.4635 |
      \n| 184 | Accept | 9.3841 | 1.4813 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 299 |
      \n| | | | | | | | MinLeafSize: 1158 |
      \n| 185 | Accept | 8.9781 | 0.071583 | 8.8186 | 643 | tree | MinLeafSize: 25 |
      \n| 186 | Accept | 9.4077 | 0.06309 | 8.8186 | 161 | svm | BoxConstraint: 349.21 |
      \n| | | | | | | | KernelScale: 0.042446 |
      \n| | | | | | | | Epsilon: 9446.5 |
      \n| 187 | Accept | 63.652 | 0.51835 | 8.8186 | 161 | svm | BoxConstraint: 55.367 |
      \n| | | | | | | | KernelScale: 2.9867 |
      \n| | | | | | | | Epsilon: 0.37288 |
      \n| 188 | Accept | 9.4193 | 0.057529 | 8.8186 | 161 | svm | BoxConstraint: 22.899 |
      \n| | | | | | | | KernelScale: 0.0048942 |
      \n| | | | | | | | Epsilon: 483.9 |
      \n| 189 | Accept | 36.23 | 0.50743 | 8.8186 | 161 | svm | BoxConstraint: 0.5866 |
      \n| | | | | | | | KernelScale: 9.2803 |
      \n| | | | | | | | Epsilon: 21.876 |
      \n| 190 | Accept | 9.1316 | 0.079127 | 8.8186 | 643 | tree | MinLeafSize: 9 |
      \n| 191 | Accept | 8.84 | 3.7635 | 8.8186 | 2569 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 273 |
      \n| | | | | | | | MinLeafSize: 4 |
      \n| 192 | Accept | 9.3821 | 1.563 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 286 |
      \n| | | | | | | | MinLeafSize: 584 |
      \n| 193 | Accept | 8.9676 | 1.8838 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 267 |
      \n| | | | | | | | MinLeafSize: 19 |
      \n| 194 | Accept | 9.1405 | 0.069161 | 8.8186 | 161 | tree | MinLeafSize: 7 |
      \n| 195 | Accept | 9.4212 | 0.09857 | 8.8186 | 161 | svm | BoxConstraint: 50.571 |
      \n| | | | | | | | KernelScale: 0.024255 |
      \n| | | | | | | | Epsilon: 7431.5 |
      \n| 196 | Accept | 8.9856 | 3.4297 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 267 |
      \n| | | | | | | | MinLeafSize: 19 |
      \n| 197 | Accept | 9.0698 | 1.7118 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 237 |
      \n| | | | | | | | MinLeafSize: 3 |
      \n| 198 | Accept | 9.3841 | 1.1616 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 219 |
      \n| | | | | | | | MinLeafSize: 135 |
      \n| 199 | Accept | 9.3855 | 1.2281 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 220 |
      \n| | | | | | | | MinLeafSize: 1640 |
      \n| 200 | Accept | 9.3889 | 0.066239 | 8.8186 | 161 | svm | BoxConstraint: 0.79242 |
      \n| | | | | | | | KernelScale: 0.02442 |
      \n| | | | | | | | Epsilon: 3825.6 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 201 | Accept | 8.9793 | 3.1501 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 237 |
      \n| | | | | | | | MinLeafSize: 3 |
      \n| 202 | Accept | 9.4226 | 0.058343 | 8.8186 | 161 | svm | BoxConstraint: 0.0095052 |
      \n| | | | | | | | KernelScale: 41.559 |
      \n| | | | | | | | Epsilon: 1783.5 |
      \n| 203 | Accept | 10.347 | 4.2165 | 8.8186 | 161 | svm | BoxConstraint: 131.23 |
      \n| | | | | | | | KernelScale: 0.072051 |
      \n| | | | | | | | Epsilon: 7.455 |
      \n| 204 | Accept | 9.3921 | 0.065431 | 8.8186 | 161 | tree | MinLeafSize: 2331 |
      \n| 205 | Accept | 9.3958 | 0.066415 | 8.8186 | 161 | svm | BoxConstraint: 0.0016799 |
      \n| | | | | | | | KernelScale: 425.35 |
      \n| | | | | | | | Epsilon: 258.47 |
      \n| 206 | Accept | 9.1869 | 0.07482 | 8.8186 | 643 | tree | MinLeafSize: 7 |
      \n| 207 | Accept | 9.3103 | 0.046643 | 8.8186 | 161 | tree | MinLeafSize: 58 |
      \n| 208 | Accept | 9.3878 | 0.04433 | 8.8186 | 161 | tree | MinLeafSize: 1330 |
      \n| 209 | Accept | 9.4127 | 0.062485 | 8.8186 | 161 | svm | BoxConstraint: 0.33434 |
      \n| | | | | | | | KernelScale: 0.015733 |
      \n| | | | | | | | Epsilon: 2799.4 |
      \n| 210 | Accept | 36.153 | 0.62403 | 8.8186 | 161 | svm | BoxConstraint: 0.1378 |
      \n| | | | | | | | KernelScale: 7.1397 |
      \n| | | | | | | | Epsilon: 15.041 |
      \n| 211 | Accept | 8.9388 | 0.059248 | 8.8186 | 643 | tree | MinLeafSize: 58 |
      \n| 212 | Accept | 8.9134 | 0.090664 | 8.8186 | 2569 | tree | MinLeafSize: 65 |
      \n| 213 | Accept | 9.3964 | 0.061733 | 8.8186 | 161 | svm | BoxConstraint: 0.26343 |
      \n| | | | | | | | KernelScale: 0.00887 |
      \n| | | | | | | | Epsilon: 3917.2 |
      \n| 214 | Accept | 9.3912 | 0.0468 | 8.8186 | 161 | tree | MinLeafSize: 2438 |
      \n| 215 | Accept | 12.36 | 0.56796 | 8.8186 | 161 | svm | BoxConstraint: 577.35 |
      \n| | | | | | | | KernelScale: 30.71 |
      \n| | | | | | | | Epsilon: 1.0514 |
      \n| 216 | Accept | 9.1224 | 1.7567 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 249 |
      \n| | | | | | | | MinLeafSize: 44 |
      \n| 217 | Accept | 9.0025 | 3.6564 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 249 |
      \n| | | | | | | | MinLeafSize: 44 |
      \n| 218 | Accept | 9.3834 | 1.1499 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 228 |
      \n| | | | | | | | MinLeafSize: 102 |
      \n| 219 | Accept | 9.028 | 2.0525 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 253 |
      \n| | | | | | | | MinLeafSize: 2 |
      \n| 220 | Accept | 9.3824 | 0.060217 | 8.8186 | 161 | tree | MinLeafSize: 374 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 221 | Accept | 9.3911 | 0.085777 | 8.8186 | 161 | svm | BoxConstraint: 12.507 |
      \n| | | | | | | | KernelScale: 0.012484 |
      \n| | | | | | | | Epsilon: 227.96 |
      \n| 222 | Accept | 8.9964 | 3.5015 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 253 |
      \n| | | | | | | | MinLeafSize: 2 |
      \n| 223 | Accept | 9.1692 | 0.06141 | 8.8186 | 161 | tree | MinLeafSize: 9 |
      \n| 224 | Accept | 54.023 | 0.53103 | 8.8186 | 161 | svm | BoxConstraint: 402.26 |
      \n| | | | | | | | KernelScale: 23.129 |
      \n| | | | | | | | Epsilon: 0.15314 |
      \n| 225 | Accept | 9.3834 | 0.061383 | 8.8186 | 161 | tree | MinLeafSize: 1 |
      \n| 226 | Accept | 8.9297 | 0.050965 | 8.8186 | 161 | tree | MinLeafSize: 30 |
      \n| 227 | Accept | 8.9426 | 0.069941 | 8.8186 | 643 | tree | MinLeafSize: 30 |
      \n| 228 | Accept | 9.3909 | 1.2347 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 242 |
      \n| | | | | | | | MinLeafSize: 193 |
      \n| 229 | Accept | 14.093 | 0.51359 | 8.8186 | 161 | svm | BoxConstraint: 2.7008 |
      \n| | | | | | | | KernelScale: 8.988 |
      \n| | | | | | | | Epsilon: 0.31364 |
      \n| 230 | Accept | 8.9475 | 1.8933 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 274 |
      \n| | | | | | | | MinLeafSize: 2 |
      \n| 231 | Accept | 9.3847 | 0.060031 | 8.8186 | 161 | tree | MinLeafSize: 5326 |
      \n| 232 | Accept | 8.8871 | 2.3958 | 8.8186 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 274 |
      \n| | | | | | | | MinLeafSize: 2 |
      \n| 233 | Accept | 8.8526 | 3.8394 | 8.8186 | 2569 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 274 |
      \n| | | | | | | | MinLeafSize: 2 |
      \n| 234 | Accept | 9.6167 | 0.71275 | 8.8186 | 161 | svm | BoxConstraint: 0.0033201 |
      \n| | | | | | | | KernelScale: 11.038 |
      \n| | | | | | | | Epsilon: 6.2594 |
      \n| 235 | Accept | 9.3917 | 0.056137 | 8.8186 | 161 | tree | MinLeafSize: 114 |
      \n| 236 | Accept | 45.36 | 4.8199 | 8.8186 | 161 | svm | BoxConstraint: 947.1 |
      \n| | | | | | | | KernelScale: 0.01755 |
      \n| | | | | | | | Epsilon: 38.99 |
      \n| 237 | Accept | 32.375 | 0.43733 | 8.8186 | 161 | svm | BoxConstraint: 80.29 |
      \n| | | | | | | | KernelScale: 131.32 |
      \n| | | | | | | | Epsilon: 1.4516 |
      \n| 238 | Accept | 9.1149 | 0.072948 | 8.8186 | 643 | tree | MinLeafSize: 9 |
      \n| 239 | Accept | 9.3992 | 0.058396 | 8.8186 | 161 | svm | BoxConstraint: 0.0087101 |
      \n| | | | | | | | KernelScale: 0.049442 |
      \n| | | | | | | | Epsilon: 3014.3 |
      \n| 240 | Accept | 32.828 | 0.68213 | 8.8186 | 161 | svm | BoxConstraint: 0.01464 |
      \n| | | | | | | | KernelScale: 30.001 |
      \n| | | | | | | | Epsilon: 34.092 |
      \n|=============================================================================================================================================|
      \n| Iter | Eval | log(1+valLoss)| Time for training | Observed min | Training set | Learner | Hyperparameter: Value |
      \n| | result | | & validation (sec)| validation loss | size | | |
      \n|=============================================================================================================================================|
      \n| 241 | Accept | 76.162 | 5.2571 | 8.8186 | 161 | svm | BoxConstraint: 25.679 |
      \n| | | | | | | | KernelScale: 0.058947 |
      \n| | | | | | | | Epsilon: 5.5863 |
      \n| 242 | Accept | 9.0454 | 2.2506 | 8.8186 | 161 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 275 |
      \n| | | | | | | | MinLeafSize: 2 |
      \n| 243 | Accept | 9.0188 | 4.1799 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 275 |
      \n| | | | | | | | MinLeafSize: 2 |
      \n| 244 | Accept | 9.3987 | 0.071851 | 8.8186 | 161 | svm | BoxConstraint: 345.64 |
      \n| | | | | | | | KernelScale: 0.90102 |
      \n| | | | | | | | Epsilon: 370.38 |
      \n| 245 | Accept | 49.943 | 0.60096 | 8.8186 | 161 | svm | BoxConstraint: 391.91 |
      \n| | | | | | | | KernelScale: 3.856 |
      \n| | | | | | | | Epsilon: 12.255 |
      \n| 246 | Accept | 9.4879 | 0.084173 | 8.8186 | 161 | tree | MinLeafSize: 2 |
      \n| 247 | Accept | 9.3865 | 1.404 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 263 |
      \n| | | | | | | | MinLeafSize: 255 |
      \n| 248 | Accept | 9.3816 | 1.6134 | 8.8186 | 643 | ensemble | Method: LSBoost |
      \n| | | | | | | | NumLearningCycles: 286 |
      \n| | | | | | | | MinLeafSize: 584 |
      \n| 249 | Accept | 45.435 | 0.10098 | 8.8186 | 161 | svm | BoxConstraint: 0.005269 |
      \n| | | | | | | | KernelScale: 0.0040109 |
      \n| | | | | | | | Epsilon: 86.961 |
      \n| 250 | Accept | 9.3853 | 1.4575 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 280 |
      \n| | | | | | | | MinLeafSize: 290 |
      \n| 251 | Accept | 9.6044 | 0.69076 | 8.8186 | 161 | svm | BoxConstraint: 291.8 |
      \n| | | | | | | | KernelScale: 755.95 |
      \n| | | | | | | | Epsilon: 1.3387 |
      \n| 252 | Accept | 9.1305 | 2.1799 | 8.8186 | 161 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 296 |
      \n| | | | | | | | MinLeafSize: 7 |
      \n| 253 | Accept | 8.8709 | 2.5698 | 8.8186 | 643 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 296 |
      \n| | | | | | | | MinLeafSize: 7 |
      \n| 254 | Accept | 8.8373 | 3.8823 | 8.8186 | 2569 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 296 |
      \n| | | | | | | | MinLeafSize: 7 |
      \n| 255 | Accept | 8.8187 | 6.4895 | 8.8186 | 10276 | ensemble | Method: Bag |
      \n| | | | | | | | NumLearningCycles: 296 |
      \n| | | | | | | | MinLeafSize: 7 |__________________________________________________________
      \nOptimization completed.
      \nTotal iterations: 255
      \nTotal elapsed time: 348.5896 seconds
      \nTotal time for training and validation: 307.7084 secondsBest observed learner is an ensemble model with:
      \nLearner: ensemble
      \nMethod: Bag
      \nNumLearningCycles: 208
      \nMinLeafSize: 16
      \nObserved log(1 + valLoss): 8.8186
      \nTime for training and validation: 4.6234 secondsDocumentation for fitrauto display<\/div>\n<\/div>\n
      \n
      <\/div>\n
      <\/div>\n
      <\/div>\n
      <\/div>\n<\/div>\n
      \n
      \n
      Mdl = <\/span><\/div>\n
      CompactRegressionEnsemble
      \nPredictorNames: {1\u00d730 cell}
      \nResponseName: ‘metastatic_diagnosis_period’
      \nCategoricalPredictors: [1 2]
      \nResponseTransform: ‘none’
      \nNumTrained: 208Properties, Methods<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
      Now I have a trained Compact Regression Ensemble model! If you wanted to explore machine learning options interactively, check out the documentation<\/a> and video<\/a> for the Regression Learner app, which allows you to rapidly prototype, modify, and explore regression models.<\/div>\n

      Create Submission<\/h2>\n
      Once you have a model that performs well, it’s time to create a submission for the datathon! As a reminder, you will upload this file to Kaggle to be scored on the leaderboard.<\/div>\n
      First, import the challenge test dataset:<\/div>\n
      \n
      \n
      testDataFilename = ‘test.csv’<\/span>;<\/span><\/div>\n<\/div>\n
      \n
      allTestData = readtable(fullfile(dataFolder, testDataFilename))<\/span><\/div>\n
      \n
      \n
      \n
      Warning: Column headers from the file were modified to make them valid MATLAB identifiers before creating variable names for the table. The original column headers are saved in the VariableDescriptions property.
      Set ‘VariableNamingRule’ to ‘preserve’ to use the original column headers as table variable names.<\/div>\n
      <\/div>\n<\/div>\n<\/div>\n
      \n
      \n
      allTestData = 5646\u00d7151 table <\/span><\/div>\n
      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      <\/th>\npatient_id<\/th>\npatient_race<\/th>\npayer_type<\/th>\npatient_state<\/th>\npatient_zip3<\/th>\nRegion<\/th>\nDivision<\/th>\npatient_age<\/th>\npatient_gender<\/th>\nbmi<\/th>\nbreast_cancer_diagnosis_code<\/th>\nbreast_cancer_diagnosis_desc<\/th>\nmetastatic_cancer_diagnosis_code<\/th>\nmetastatic_first_novel_treatment<\/th>\nmetastatic_first_novel_treatment_type<\/th>\npopulation<\/th>\ndensity<\/th>\nage_median<\/th>\nage_under_10<\/th>\nage_10_to_19<\/th>\nage_20s<\/th>\nage_30s<\/th>\nage_40s<\/th>\nage_50s<\/th>\nage_60s<\/th>\nage_70s<\/th>\nage_over_80<\/th>\nmale<\/th>\nfemale<\/th>\nmarried<\/th>\n<\/tr>\n<\/thead>\n
      1<\/th>\n730681<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘LA’<\/td>\n713<\/td>\n‘South’<\/td>\n‘West\u00a0South\u00a0Central’<\/td>\n55<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘1746’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0axillary\u00a0tail\u00a0of\u00a0female\u00a0breast’<\/td>\n‘C7981’<\/td>\nNaN<\/td>\nNaN<\/td>\n4.6391e+03<\/td>\n72.6643<\/td>\n41.5000<\/td>\n11.3952<\/td>\n13.4357<\/td>\n11.4214<\/td>\n11.4452<\/td>\n12.5619<\/td>\n13.0786<\/td>\n14.2571<\/td>\n7.7071<\/td>\n4.7286<\/td>\n50.0191<\/td>\n49.9809<\/td>\n42.3738<\/td>\n<\/tr>\n
      2<\/th>\n334212<\/td>\n‘Black’<\/td>\n”<\/td>\n‘NC’<\/td>\n283<\/td>\n‘South’<\/td>\n‘South\u00a0Atlantic’<\/td>\n60<\/td>\n‘F’<\/td>\n40<\/td>\n‘C50912’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n1.0875e+04<\/td>\n217.9104<\/td>\n39.6447<\/td>\n11.2329<\/td>\n13.7158<\/td>\n15.0053<\/td>\n12.0158<\/td>\n11.5803<\/td>\n11.7711<\/td>\n12.7684<\/td>\n8.5184<\/td>\n3.4066<\/td>\n51.3263<\/td>\n48.6737<\/td>\n44.1355<\/td>\n<\/tr>\n
      3<\/th>\n571362<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘TX’<\/td>\n794<\/td>\n‘South’<\/td>\n‘West\u00a0South\u00a0Central’<\/td>\n54<\/td>\n‘F’<\/td>\n32.3300<\/td>\n‘1742’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0upper-inner\u00a0quadrant\u00a0of\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n1.8717e+04<\/td>\n1.0195e+03<\/td>\n30.3714<\/td>\n11<\/td>\n18.8643<\/td>\n23.1143<\/td>\n12.2429<\/td>\n9.8786<\/td>\n9.1214<\/td>\n8.3786<\/td>\n4.7786<\/td>\n2.6214<\/td>\n50.2857<\/td>\n49.7143<\/td>\n35.9857<\/td>\n<\/tr>\n
      4<\/th>\n907331<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘TN’<\/td>\n373<\/td>\n‘South’<\/td>\n‘East\u00a0South\u00a0Central’<\/td>\n63<\/td>\n‘F’<\/td>\n27.0700<\/td>\n‘1748’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0other\u00a0specified\u00a0sites\u00a0of\u00a0female\u00a0breast’<\/td>\n‘C7951’<\/td>\nNaN<\/td>\nNaN<\/td>\n7.8048e+03<\/td>\n140.0545<\/td>\n44.3158<\/td>\n10.1947<\/td>\n12.6645<\/td>\n11.7026<\/td>\n10.5250<\/td>\n12.1329<\/td>\n14.9132<\/td>\n13.6816<\/td>\n9.8263<\/td>\n4.3632<\/td>\n49.4066<\/td>\n50.5934<\/td>\n52.2210<\/td>\n<\/tr>\n
      5<\/th>\n208382<\/td>\n‘Asian’<\/td>\n”<\/td>\n‘WA’<\/td>\n980<\/td>\n‘West’<\/td>\n‘Pacific’<\/td>\n62<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘C50411’<\/td>\n‘Malig\u00a0neoplm\u00a0of\u00a0upper-outer\u00a0quadrant\u00a0of\u00a0right\u00a0female\u00a0breast’<\/td>\n‘C787’<\/td>\nNaN<\/td>\nNaN<\/td>\n2.8628e+04<\/td>\n1.0918e+03<\/td>\n39.6793<\/td>\n12.1434<\/td>\n12.4623<\/td>\n11.3208<\/td>\n15.2132<\/td>\n14.4491<\/td>\n14.1057<\/td>\n11.2264<\/td>\n5.8415<\/td>\n3.2302<\/td>\n49.9698<\/td>\n50.0302<\/td>\n57.0962<\/td>\n<\/tr>\n
      6<\/th>\n852863<\/td>\n‘White’<\/td>\n‘MEDICARE\u00a0ADVANTAGE’<\/td>\n‘CA’<\/td>\n914<\/td>\n‘West’<\/td>\n‘Pacific’<\/td>\n82<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘1749’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified’<\/td>\n‘C7951’<\/td>\nNaN<\/td>\nNaN<\/td>\n3.9505e+04<\/td>\n4.0085e+03<\/td>\n37.5500<\/td>\n11.4875<\/td>\n11.4375<\/td>\n14.5125<\/td>\n16.6125<\/td>\n14.2875<\/td>\n13.6500<\/td>\n9.4750<\/td>\n5.3500<\/td>\n3.2250<\/td>\n49.6500<\/td>\n50.3500<\/td>\n43.3875<\/td>\n<\/tr>\n
      7<\/th>\n494644<\/td>\n‘Asian’<\/td>\n”<\/td>\n‘MI’<\/td>\n483<\/td>\n‘Midwest’<\/td>\n‘East\u00a0North\u00a0Central’<\/td>\n67<\/td>\n‘F’<\/td>\n21.8000<\/td>\n‘C50911’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0right\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n2.0151e+04<\/td>\n724.9353<\/td>\n42.0784<\/td>\n11.0392<\/td>\n13.0098<\/td>\n11.6431<\/td>\n11.8882<\/td>\n13.0647<\/td>\n15.1098<\/td>\n12.8686<\/td>\n7.4000<\/td>\n3.9588<\/td>\n49.2922<\/td>\n50.7078<\/td>\n54.0137<\/td>\n<\/tr>\n
      8<\/th>\n852015<\/td>\n‘White’<\/td>\n‘MEDICAID’<\/td>\n‘FL’<\/td>\n336<\/td>\n‘South’<\/td>\n‘South\u00a0Atlantic’<\/td>\n51<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘C50919’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0unsp\u00a0site\u00a0of\u00a0unspecified\u00a0female\u00a0breast’<\/td>\n‘C7931’<\/td>\nNaN<\/td>\nNaN<\/td>\n3.0205e+04<\/td>\n1.5172e+03<\/td>\n35.6296<\/td>\n11.6963<\/td>\n14.2296<\/td>\n16.5926<\/td>\n15.2518<\/td>\n12.9037<\/td>\n11.6296<\/td>\n9.5259<\/td>\n5.4667<\/td>\n2.7444<\/td>\n49.6963<\/td>\n50.3037<\/td>\n39.3148<\/td>\n<\/tr>\n
      9<\/th>\n521061<\/td>\n‘Black’<\/td>\n‘MEDICAID’<\/td>\n‘CA’<\/td>\n917<\/td>\n‘West’<\/td>\n‘Pacific’<\/td>\n44<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘C50011’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0nipple\u00a0and\u00a0areola,\u00a0right\u00a0female\u00a0breast’<\/td>\n‘C779’<\/td>\nNaN<\/td>\nNaN<\/td>\n4.3030e+04<\/td>\n2.0486e+03<\/td>\n38.8522<\/td>\n11.3065<\/td>\n12.8978<\/td>\n14.1217<\/td>\n13.5326<\/td>\n13.1609<\/td>\n13.3783<\/td>\n11.4739<\/td>\n6.3804<\/td>\n3.7370<\/td>\n49.0522<\/td>\n50.9478<\/td>\n48.5044<\/td>\n<\/tr>\n
      10<\/th>\n907023<\/td>\n‘White’<\/td>\n”<\/td>\n‘PA’<\/td>\n160<\/td>\n‘Northeast’<\/td>\n‘Middle\u00a0Atlantic’<\/td>\n70<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘C50812’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0ovrlp\u00a0sites\u00a0of\u00a0left\u00a0female\u00a0breast’<\/td>\n‘C7951’<\/td>\nNaN<\/td>\nNaN<\/td>\n5.8126e+03<\/td>\n130.5714<\/td>\n44.6743<\/td>\n10.2943<\/td>\n12.1914<\/td>\n10.6971<\/td>\n11.6086<\/td>\n12.4543<\/td>\n14.5114<\/td>\n15.5171<\/td>\n8.0343<\/td>\n4.6629<\/td>\n50.5486<\/td>\n49.4514<\/td>\n56.5857<\/td>\n<\/tr>\n
      11<\/th>\n906063<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘TX’<\/td>\n774<\/td>\n‘South’<\/td>\n‘West\u00a0South\u00a0Central’<\/td>\n27<\/td>\n‘F’<\/td>\n27.3700<\/td>\n‘C50912’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0unspecified\u00a0site\u00a0of\u00a0left\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n1.9403e+04<\/td>\n270.8549<\/td>\n39.1349<\/td>\n12.5188<\/td>\n15.7422<\/td>\n12.6547<\/td>\n13.3703<\/td>\n10.0297<\/td>\n12.6359<\/td>\n10.6875<\/td>\n7.1516<\/td>\n5.1937<\/td>\n50.0281<\/td>\n49.9719<\/td>\n51.7047<\/td>\n<\/tr>\n
      12<\/th>\n558053<\/td>\n‘Hispanic’<\/td>\n‘MEDICAID’<\/td>\n‘DE’<\/td>\n199<\/td>\n‘South’<\/td>\n‘South\u00a0Atlantic’<\/td>\n44<\/td>\n‘F’<\/td>\n33.2000<\/td>\n‘C50112’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0central\u00a0portion\u00a0of\u00a0left\u00a0female\u00a0breast’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n1.0754e+04<\/td>\n180.9974<\/td>\n45.9846<\/td>\n10.3359<\/td>\n11.5462<\/td>\n11.4692<\/td>\n10.6795<\/td>\n9.9436<\/td>\n14.4461<\/td>\n15.7180<\/td>\n10.7692<\/td>\n5.0949<\/td>\n50.7026<\/td>\n49.2974<\/td>\n50.9436<\/td>\n<\/tr>\n
      13<\/th>\n832804<\/td>\n‘White’<\/td>\n‘MEDICARE\u00a0ADVANTAGE’<\/td>\n‘OH’<\/td>\n442<\/td>\n‘Midwest’<\/td>\n‘East\u00a0North\u00a0Central’<\/td>\n82<\/td>\n‘F’<\/td>\nNaN<\/td>\n‘19881’<\/td>\n‘Secondary\u00a0malignant\u00a0neoplasm\u00a0of\u00a0breast’<\/td>\n‘C7951’<\/td>\nNaN<\/td>\nNaN<\/td>\n13035<\/td>\n355.7023<\/td>\n42.8907<\/td>\n10.5953<\/td>\n14.0861<\/td>\n11.4395<\/td>\n11.4233<\/td>\n11.4302<\/td>\n14.9023<\/td>\n13.5488<\/td>\n8.5814<\/td>\n4.0023<\/td>\n49.9279<\/td>\n50.0721<\/td>\n54.1256<\/td>\n<\/tr>\n
      14<\/th>\n554976<\/td>\n”<\/td>\n‘COMMERCIAL’<\/td>\n‘MT’<\/td>\n591<\/td>\n‘West’<\/td>\n‘Mountain’<\/td>\n71<\/td>\n‘F’<\/td>\n23.4800<\/td>\n‘1749’<\/td>\n‘Malignant\u00a0neoplasm\u00a0of\u00a0breast\u00a0(female),\u00a0unspecified’<\/td>\n‘C773’<\/td>\nNaN<\/td>\nNaN<\/td>\n35549<\/td>\n367.6250<\/td>\n38.3250<\/td>\n13.0250<\/td>\n13.3000<\/td>\n12<\/td>\n14<\/td>\n12.6500<\/td>\n11.5500<\/td>\n12.5750<\/td>\n6.7500<\/td>\n4.1500<\/td>\n49.6000<\/td>\n50.4000<\/td>\n51.4750<\/td>\n<\/tr>\n
      \u22ee<\/th>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
      <\/div>\n
      <\/div>\n
      <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
      Then we need to process this dataset in the same way that we did the training data. In this section, I use code instead of the live tasks for simplicity.<\/div>\n
      \n
      \n
      % replace cell arrays with categoricals<\/span><\/span><\/div>\n<\/div>\n
      \n
      varTypes = varfun(@class, allTestData, OutputFormat=“cell”<\/span>);<\/span><\/div>\n<\/div>\n
      \n
      catIdx = strcmp(varTypes, “cell”<\/span>);<\/span><\/div>\n<\/div>\n
      \n
      varNames = allTestData.Properties.VariableNames;<\/span><\/div>\n<\/div>\n
      \n
      catVarNames = varNames(catIdx);<\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      for <\/span>catNameIdx = 1:length(catVarNames)<\/span><\/div>\n<\/div>\n
      \n
      allTestData.(catVarNames{catNameIdx}) = categorical(allTestData.(catVarNames{catNameIdx}));<\/span><\/div>\n<\/div>\n
      \n
      end<\/span><\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      % remove variables with too many missing data points<\/span><\/span><\/div>\n<\/div>\n
      \n
      allTestData = removevars(allTestData, [“patient_race”<\/span>, “bmi”<\/span>, “metastatic_first_novel_treatment”<\/span>, “metastatic_first_novel_treatment_type”<\/span>]);<\/span><\/div>\n<\/div>\n
      \n
      <\/div>\n<\/div>\n
      \n
      % add ‘yearsFromMeanAge’ variable<\/span><\/span><\/div>\n<\/div>\n
      \n
      meanAge = mean(allTestData.patient_age);<\/span><\/div>\n<\/div>\n
      \n
      yearsFromMeanAge = allTestData.patient_age – meanAge;<\/span><\/div>\n<\/div>\n
      \n
      allTestData = addvars(allTestData, yearsFromMeanAge);<\/span><\/div>\n<\/div>\n<\/div>\n
      We also need to use the transform<\/span><\/a> function to create the same features as we created using genrfeatures<\/span> for the training data.<\/div>\n
      \n
      \n
      augTestData = transform(T, allTestData);<\/span><\/div>\n<\/div>\n<\/div>\n
      Now that the data is in the format our machine learning model expects it to be in, use the predict<\/span><\/a> function to make predictions, and create a table to contain the patient IDs and corresponding predictions.<\/div>\n
      \n
      \n
      submissionPreds = predict(Mdl, augTestData);<\/span><\/div>\n<\/div>\n
      \n
      submissionTable = table(allTestData.patient_id, submissionPreds, VariableNames=[“patient_id”<\/span>, “metastatic_diagnosis_period”<\/span>])<\/span><\/div>\n
      \n
      \n
      \n
      submissionTable = 5646\u00d72 table <\/span><\/div>\n
      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      <\/th>\npatient_id<\/th>\nmetastatic_diagnosis_period<\/th>\n<\/tr>\n<\/thead>\n
      1<\/th>\n730681<\/td>\n214.2833<\/td>\n<\/tr>\n
      2<\/th>\n334212<\/td>\n57.7037<\/td>\n<\/tr>\n
      3<\/th>\n571362<\/td>\n223.1813<\/td>\n<\/tr>\n
      4<\/th>\n907331<\/td>\n219.9710<\/td>\n<\/tr>\n
      5<\/th>\n208382<\/td>\n55.4499<\/td>\n<\/tr>\n
      6<\/th>\n852863<\/td>\n216.6935<\/td>\n<\/tr>\n
      7<\/th>\n494644<\/td>\n56.6413<\/td>\n<\/tr>\n
      8<\/th>\n852863<\/td>\n216.6935<\/td>\n<\/tr>\n
      9<\/th>\n521061<\/td>\n68.0861<\/td>\n<\/tr>\n
      10<\/th>\n907023<\/td>\n53.8339<\/td>\n<\/tr>\n
      11<\/th>\n906063<\/td>\n67.2746<\/td>\n<\/tr>\n
      12<\/th>\n558053<\/td>\n67.3586<\/td>\n<\/tr>\n
      13<\/th>\n832804<\/td>\n205.2770<\/td>\n<\/tr>\n
      14<\/th>\n554976<\/td>\n208.7227<\/td>\n<\/tr>\n
      \u22ee<\/th>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
      <\/div>\n
      <\/div>\n
      <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
      Last, export your predictions to a .CSV file, then upload to Kaggle for scoring.<\/div>\n
      \n
      \n
      writetable(submissionTable, “Predictions.csv”<\/span>);<\/span><\/div>\n<\/div>\n<\/div>\n
      Thank you for following along with this tutorial, and best of luck to all participants. If you have any questions about this tutorial or MATLAB, reach out to us at studentcompetitions@mathworks.com<\/span><\/a> or by tagging gracewoolson<\/a> in the forum. Keep your eye out for our upcoming livestream on the MATLAB YouTube channel<\/a> on April 18th, where we will walk through this tutorial and answer any questions you have along the way!<\/div>\n<\/div>\n