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%[text] # Introduction
%[text] This challenge focuses on ensuring patient health through data. You are tasked to train a model that takes as input the patient record data and outputs a prediction of whether or not the patient has Diabetes Mellitus, which could inform treatment in the ICU. Learn more about and download the challenge dataset by registering for [this Kaggle competition](https://www.kaggle.com/competitions/widsdatathon2021/data) and downloading the training dataset. You may also choose to download the data dictionary, which contains additional information about the dataset.
%[text] This tutorial uses an example dataset to show you how to complete the steps of building your own regression model, so **make sure to replace this dataset with the challenge dataset before making your own models.**
%[text] # Load & Prepare Data
%[text] This dataset presents an opportunity to learn about the data modelling and processing challenges a real-world data problem brings. This tutorial will show some basic methods to handle data challenges, but you can learn about more methods by going through this [Data Science Tutorial video series](https://www.mathworks.com/videos/series/data-science-tutorial.html).
%[text] ## Step 1: Load Data
%[text] Use the [readtable](https://www.mathworks.com/help/matlab/ref/readtable.html?) function to read the files and store it as tables. You can also import data using the MATLAB® [Import tool](https://www.mathworks.com/help/matlab/import_export/select-spreadsheet-data-interactively.html).
fullSet = readtable('exampleTrain.csv');
%[text] Once imported, preview the data to get a sense of what you're working with.
head(fullSet) %[output:75c3c72b]
%%
%[text] ## Step 2: Clean Data
%[text] The biggest challenge with real datasets is that they are messy. Data transformation and modelling will be the key area to work on to avoid overfitting the problem.
%[text] Using the *summary* function, analyze the types of the predictors, some statistics about each predictor, and the number of missing values for each predictor column. This can help you decide how to clean your dataset.
summary(fullSet); %[output:9b5b9f21]
%[text] There are many different approaches to work with the missing values and predictor selection. We will go through one of the basic approaches, but you can also refer to this document to learn about other methods: [Clean Messy and Missing Data](https://www.mathworks.com/help/stats/find-missing-data.html).
%[text] *Note: This approach of data cleaning demonstrated is chosen arbitrarily to cut down number of predictor columns.*
%%
%[text] **Remove the character columns of the table**
%[text] Many machine learning algorithms only allow for numeric values as the input arguments, so you can remove variables of any non-numeric type. Those are imported as cell arrays, so check which columns are of type 'cell' and remove them.
columnDataTypes = varfun(@iscell, fullSet, OutputFormat="cell");
nonNumericCols = cell2mat(columnDataTypes);
nonNumColNames = fullSet.Properties.VariableNames(nonNumericCols);
fullSet = removevars(fullSet, nonNumColNames) %[output:766f1a15]
%%
%[text] **Remove extreme values from all the vitals predictors**
%[text] Some data points may be on the extreme low or high end of what you'd expect to see, which might cause unexpected behavior when this data is used to train a model. Use the Clean Outlier Data live task to remove these outlier data points.
% Remove outliers %[task:83a5]
[cleanedSet,outlierIndices,outliersForPlot2,lo2,hi2] = rmoutliers(fullSet, ...
"percentiles",[1 99]);
% Display results
figure %[output:86cd58e5]
plot(fullSet.Var1,"SeriesIndex",6,"DisplayName","Input data") %[output:86cd58e5]
hold on %[output:86cd58e5]
plot(find(~outlierIndices),cleanedSet.Var1,"SeriesIndex",1,"LineWidth",1.5, ... %[output:86cd58e5]
"DisplayName","Cleaned data") %[output:86cd58e5]
% Plot outliers
plot(find(outliersForPlot2(:,1)),fullSet.Var1(outliersForPlot2(:,1)),"x", ... %[output:86cd58e5]
"SeriesIndex",2,"DisplayName","Outliers") %[output:86cd58e5]
% Plot data in rows where other variables contain outliers
mask = outlierIndices & ~outliersForPlot2(:,1);
plot(find(mask),fullSet.Var1(mask),"x","SeriesIndex","none", ... %[output:86cd58e5]
"DisplayName","Removed by other variables") %[output:86cd58e5]
% Plot outlier thresholds
plot([xlim missing xlim],[lo2.Var1 lo2.Var1 missing hi2.Var1 hi2.Var1], ... %[output:86cd58e5]
"Color",[145 145 145]/255,"DisplayName","Outlier thresholds") %[output:86cd58e5]
hold off %[output:86cd58e5]
title("Number of outliers cleaned: " + nnz(outliersForPlot2(:,1))) %[output:86cd58e5]
legend %[output:86cd58e5]
ylabel("Var1") %[output:86cd58e5]
clear outliersForPlot2 lo2 hi2 mask %[task:83a5]
%%
%[text] **Remove the rows which have at least 10 missing values**
%[text] The other assumption I made is the observations (rows) which have missing predictor values can be removed.
cleanedSet = rmmissing(cleanedSet,1,"MinNumMissing",10);
%%
%[text] **Move response variable to the end**
%[text] Last, move our label predictor *OutputVar* to the last column of the table because for some algorithms in MATLAB the last column is the default response variable.
cleanedSet = movevars(cleanedSet,'OutputVar') %[output:4fd33038]
%%
%[text] ## Step 3: Create Training Data
%[text] Once the data is clean, separate the output variable *OutputVar* from the dataset and create two separate tables. *XSet:* Predictor data, *YSet*: Class labels
XSet = removevars(cleanedSet,{'OutputVar'});
YSet = cleanedSet.OutputVar;
%%
%[text] ## Step 4: Create Test Data
%[text] Set aside a portion of the dataset to evaluate your trained models, the rest will be used for training. You can adjust how much you hold out for testing.
c = cvpartition(height(XSet), 'Holdout', 0.2);
idxTrain = training(c);
XTrain = XSet(idxTrain,:);
YTrain = YSet(idxTrain);
idxTest = test(c);
XTest = XSet(idxTest,:);
YTest = YSet(idxTest);
%%
%[text] ## Step 5: Train a Model
%[text] In MATLAB you can train a classification model using two different methods:
%[text] 1. Using MATLAB machine learning algorithm functions
%[text] 2. Using the Classification Learner app \
%[text] ## Option 1: Using custom algorithms
%[text] A Binary classification problem can be approached using various algorithms like decision tress, svm, and logistic regression. Here I train using [fitclinear](https://www.mathworks.com/help/stats/fitclinear.html?) classification model. It trains the linear binary classification models with high dimensional predictor data.
%[text] Convert the table to a numeric matrix because *fitclinear* function takes only numeric matrix as an input argument.
XTrainMat = table2array(XTrain);
XTestMat = table2array(XTest);
%[text] Using Name-Value options, let's set the solver as sparsa (Sparse Reconstruction by Separable Approximation), which has default *lasso* regularization. Check out the [*fitclinear*](https://www.mathworks.com/help/stats/fitclinear.html?) document to learn more about all of the input argument options.
Mdl = fitclinear(XTrainMat,YTrain,'ObservationsIn','rows',... %[output:group:05ec747c] %[output:04359215]
'Solver','sparsa'); %[output:group:05ec747c] %[output:04359215]
%%
%[text] **Predict on the Test Set**
%[text] Once we have your model ready, you can perform predictions on your test set using predict function. It takes as input the fitted model and test data. The output is the predicted labels and scores. You can then evaluate how well the model performs by plotting a confusion matrix, which compares the predicted values to the actual values. Since this is just a dummy dataset, the performance is very poor, but when you start working with real data and testing different model configurations, you should start to see significant improvement. An ideal model would show two dark blue squares, one in the top-left and one in the bottom-right, to indicate that the model predicted correctly every time.
[label,~] = predict(Mdl,XTestMat);
confusionchart(YTest, label); %[output:10c4f4f9]
%[text] This model appears to predict 0 every time, indicating we should make some adjustments to the data, machine learning algorithm, or training options.
%%
%[text] ## Option 2: Using Classification Learner App
%[text] Second method of training the model is by using the Classification Learner app. It lets you interactively train, validate and tune classification model. Get started with it by following the steps below, and check out the [documentation](https://www.mathworks.com/help/stats/classification-learner-app.html) to learn more.
%[text] - On the ***Apps*** tab, in the Machine Learning group, click ***Classification Learner***.
%[text] - Click ***New Session*** and select data (***XTrain***) from the workspace. Specify the response variable as 'From workspace' (***YTrain***).
%[text] - Select the validation method to avoid overfitting. You can either choose ***holdout validation*** or ***cross-validation,*** selecting the number of k-folds.
%[text] - Hit ***Start Session***
%[text] - On the ***Classification Learner tab***, in the ***Model Type*** section, select the algorithm to be trained (e.g. *logistic regression, All svm, All Quick-to-train).*
%[text] - You can use the ***Options*** section to customize your algorithms throuch PCA, Feature Selection, or other advanced options.
%[text] - Once all required options are selected, click ***Train***.
%[text] - The history window on the left displays the different models trained and their accuracy.
%[text] - Performance of the model on the validation data can be evaluated by ***Confusion Matrix*** plot or other options on the ***Plots and Results*** section of the toolstrip.
%[text] - To make predictions on the test set, export the model by selecting ***Export Model*** on *Classification Learner tab*. \
%%
%[text] **Predict on the Test Set**
%[text] The exported model is saved as *trainedModel* in the workspace. You can then predict labels and scores using *predictFcn*.
%[text] The *label* is the predicted labels on Test set. *Scores* are the scores of how confident the model is that the correct output is each class. You can then evaluate how well the model performs by plotting a confusion matrix, which compares the predicted values to the actual values.
[label,~] = trainedModel.predictFcn(XTest);
confusionchart(YTest, label); %[output:6a5e24a3]
%[text] This shows that the model is predicting 0 more often than it should. You should play around with other metrics and visualizations to showcase the quality of your model.
%%
%[text] # Additional Resources
%[text] 1. [Data Science Tutorial](https://www.mathworks.com/videos/series/data-science-tutorial.html)
%[text] 2. [Missing Data in MATLAB](https://www.mathworks.com/help/releases/R2019b/matlab/data_analysis/missing-data-in-matlab.html)
%[text] 3. [Supervised Learning Workflow and Algorithms](https://www.mathworks.com/help/releases/R2019b/stats/supervised-learning-machine-learning-workflow-and-algorithms.html)
%[text] 4. [Train Classification Models in Classification Learner App](https://www.mathworks.com/help/stats/train-classification-models-in-classification-learner-app.html)
%[text] 5. [Export Classification Model to Predict New Data](https://www.mathworks.com/help/stats/export-classification-model-for-use-with-new-data.html)
%[text] 6. [8 MATLAB Cheat Sheets for Data Science](https://www.mathworks.com/campaigns/offers/data-science-cheat-sheets.html) \
%[appendix]{"version":"1.0"}
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<strong>Var82<\/strong> <strong>Var83<\/strong> <strong>Var84<\/strong> <strong>Var85<\/strong> <strong>Var86<\/strong> <strong>Var87<\/strong> <strong>Var88<\/strong> <strong>Var89<\/strong> <strong>Var90<\/strong> <strong>Var91<\/strong> <strong>Var92<\/strong> <strong>Var93<\/strong> <strong>Var94<\/strong> <strong>Var95<\/strong> <strong>Var96<\/strong> <strong>Var97<\/strong> <strong>Var98<\/strong> <strong>Var99<\/strong> <strong>Var100<\/strong> <strong>Var101<\/strong> <strong>Var102<\/strong> <strong>Var103<\/strong> <strong>Var104<\/strong> <strong>Var105<\/strong> <strong>Var106<\/strong> <strong>Var107<\/strong> <strong>Var108<\/strong> <strong>Var109<\/strong> <strong>Var110<\/strong> <strong>Var111<\/strong> <strong>Var112<\/strong> <strong>Var113<\/strong> <strong>Var114<\/strong> <strong>Var115<\/strong> <strong>Var116<\/strong> <strong>Var117<\/strong> <strong>Var118<\/strong> <strong>Var119<\/strong> <strong>Var120<\/strong> <strong>Var121<\/strong> <strong>Var122<\/strong> <strong>Var123<\/strong> <strong>Var124<\/strong> <strong>Var125<\/strong> <strong>Var126<\/strong> <strong>Var127<\/strong> <strong>Var128<\/strong> <strong>Var129<\/strong> <strong>Var130<\/strong> <strong>Var131<\/strong> <strong>Var132<\/strong> <strong>Var133<\/strong> <strong>Var134<\/strong> <strong>Var135<\/strong> <strong>Var136<\/strong> <strong>Var137<\/strong> <strong>Var138<\/strong> <strong>Var139<\/strong> <strong>Var140<\/strong> <strong>Var141<\/strong> <strong>Var142<\/strong> <strong>Var143<\/strong> <strong>Var144<\/strong> <strong>Var145<\/strong> <strong>Var146<\/strong> <strong>Var147<\/strong> <strong>Var148<\/strong> <strong>Var149<\/strong> <strong>Var150<\/strong> <strong>Var151<\/strong> <strong>Var152<\/strong> <strong>Var153<\/strong> <strong>Var154<\/strong> <strong>Var155<\/strong> <strong>Var156<\/strong> <strong>Var157<\/strong> <strong>Var158<\/strong> <strong>Var159<\/strong> <strong>Var160<\/strong> <strong>Var161<\/strong> <strong>Var162<\/strong> <strong>Var163<\/strong> <strong>Var164<\/strong> <strong>Var165<\/strong> <strong>Var166<\/strong> <strong>Var167<\/strong> <strong>Var168<\/strong> <strong>Var169<\/strong> <strong>Var170<\/strong> <strong>Var171<\/strong> <strong>Var172<\/strong> <strong>Var173<\/strong> <strong>Var174<\/strong> <strong>Var175<\/strong> <strong>Var176<\/strong> <strong>Var177<\/strong> <strong>Var178<\/strong> <strong>Var179<\/strong> <strong>Var180<\/strong> <strong>OutputVar<\/strong>\n <strong>________<\/strong> <strong>____<\/strong> <strong>________<\/strong> <strong>____<\/strong> <strong>_______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>_________<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>_______<\/strong> <strong>_____<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>_______<\/strong> <strong>_____<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>_______<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>_____<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>_____<\/strong> <strong>_______<\/strong> <strong>_______<\/strong> <strong>_______<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>_____<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>_______<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>______<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>_______<\/strong> <strong>_________<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>______<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>________<\/strong> <strong>_______<\/strong> <strong>______<\/strong> <strong>_________<\/strong>\n\n 0.52789 12 {'cat4'} 0 0.48777 {'cat7'} 83.102 0.3631 89.457 0 0 {'cat8'} 16.568 1 41.647 NaN 0.038962 7.9056 0 {'cat6'} 7 1 {'cat5'} 2 1 0.63281 8 {'cat6'} 62.318 60.941 0.81669 96.934 84.774 0.43036 35.637 63.741 0 1 0.46188 28.13 NaN 0.48646 1 72.449 {'cat3'} 54.39 11 {'cat2'} 43.984 16 8 0.31845 0.05507 61.022 0.70471 NaN NaN 0 1 9 1 14 {'cat2'} 8 {'cat8'} 45.364 5 48.219 8 {'cat4'} 0.92193 0.52353 {'cat6'} {'cat3'} 0.59936 1 {'cat5'} {'cat8'} 0 {'cat3'} 9 0.96399 58.582 10 74.086 56.202 {'cat2'} {'cat5'} 0.27694 9.2188 2 0.97698 66.478 0.14842 NaN 1 0 0 21.841 1 0.27287 5 {'cat6'} 0 92.115 4 {'cat8'} 10 1 67.916 1 {'cat1'} 0.24243 0 0.13018 34.707 {'cat6'} 1 96.626 {'cat8'} {'cat1'} 90.001 2 0.045181 48.045 0.13045 49.811 {'cat6'} 0.96426 0.12 0 36.225 1 6.9078 52.755 0.78322 73.255 NaN 13 8.0534 12 0.78525 {'cat6'} 18.817 3 {'cat4'} {'cat3'} 15.174 {'cat7'} 16 1 0.42983 59.678 33.717 9 0.099291 6 {'cat5'} {'cat2'} 4 0 1 4 {'cat7'} 0.63913 {'cat4'} 1 0.86314 0 47.613 0.0027961 76.41 0 1 {'cat6'} {'cat8'} {'cat5'} {'cat5'} 0.80393 18.224 0 \n 0.6499 2 {'cat6'} 0 0.63576 {'cat2'} 48.117 0.64575 17.929 0 0 {'cat8'} 36.978 1 67.655 NaN 0.25548 80.421 1 {'cat3'} 14 NaN {'cat1'} 6 1 0.41955 1 {'cat7'} 4.8285 44.548 0.16364 72.448 87.382 0.95234 39.586 47.942 0 0 0.78089 91.013 0.93431 0.70093 9 2.0939 {'cat2'} 92.581 3 {'cat5'} 59.084 13 1 0.074149 0.41705 84.878 0.76195 32.216 NaN 0 1 6 1 15 {'cat6'} 11 {'cat4'} 21.894 13 22.48 15 {'cat3'} 0.29022 0.96011 {'cat4'} {'cat2'} 0.044348 1 {'cat3'} {'cat6'} 1 {'cat5'} 2 0.084649 64.843 10 59.782 20.211 {'cat2'} {'cat2'} 0.38449 74.793 9 0.42207 90.004 0.98115 NaN 0 0 1 79.546 0 0.90382 7 {'cat4'} 0 96.786 7 {'cat8'} 3 1 NaN 1 {'cat4'} 0.54165 1 0.23825 17.068 {'cat2'} 1 35.777 {'cat6'} {'cat7'} 99.804 7 0.059608 65 0.4561 60.281 {'cat1'} 0.49012 0.68327 0 59.476 16 95.749 77.502 0.16564 64.077 NaN 7 98.168 2 0.82489 {'cat1'} 84.969 16 {'cat4'} {'cat5'} 3.269 {'cat1'} 9 1 0.11897 23.642 33.866 3 54.248 6 {'cat4'} {'cat7'} 13 1 0 14 {'cat1'} 0.5455 {'cat3'} 1 0.48708 1 67.287 0.71194 27.386 0 NaN {'cat7'} {'cat3'} {'cat3'} {'cat6'} 0.27057 27.126 0 \n 0.93211 16 {'cat2'} 0 0.99621 {'cat7'} 58.188 0.98365 67.868 0 0 {'cat3'} 10.902 1 85.203 0 0.04519 9.0337 1 {'cat5'} 2 1 {'cat7'} 7 1 0.94914 8 {'cat8'} 2.803 28.301 0.5898 83.307 97.988 0.35268 87.22 5.8101 0 0 0.95578 75.301 0.064654 0.32737 11 68.068 {'cat7'} 79.229 3 {'cat1'} 1.0243 15 7 0.80816 0.33786 89.325 0.87765 55.564 NaN 0 1 7 1 12 {'cat2'} 2 {'cat8'} 16.443 3 22.782 4 {'cat4'} 0.060216 0.28436 {'cat6'} {'cat2'} 0.65151 0 {'cat3'} {'cat3'} 0 {'cat4'} 6 0.92441 24.905 4 9.4327 98.275 {'cat8'} {'cat1'} 0.81228 81.966 10 0.91881 80.807 NaN 0 1 1 1 33.669 0 0.22064 3 {'cat1'} 1 4.3574 1 {'cat7'} 12 0 NaN 0 {'cat4'} 0.095456 0 0.85474 31.009 {'cat8'} 0 7.7646 {'cat1'} {'cat8'} 65.818 6 0.10142 22.481 0.71393 17.686 {'cat4'} 0.77133 NaN 0 61.292 4 56.308 81.132 0.96266 73.002 0 10 37.567 14 0.39763 {'cat1'} 62.865 12 {'cat5'} {'cat7'} 25.15 {'cat7'} 4 0 0.44475 25.825 53.181 4 31.102 1 {'cat2'} {'cat8'} 2 0 0 8 {'cat2'} 0.19809 {'cat5'} 0 0.97341 0 3.2328 0.3885 0.31632 0 1 {'cat3'} {'cat2'} {'cat2'} {'cat5'} 0.80199 26.727 0 \n 0.017386 14 {'cat8'} 0 0.74304 {'cat1'} 29.1 0.18451 47.685 1 1 {'cat8'} 3.5347 0 18.536 1 0.66559 33.767 1 {'cat3'} 9 1 {'cat7'} 15 0 0.10648 15 {'cat5'} 19.878 57.756 0.83451 22.735 16.881 0.82762 23.777 11.234 1 1 0.73245 76.396 0.89659 0.43345 6 28.654 {'cat1'} 68.249 11 {'cat8'} 31.512 5 11 0.67318 0.77196 6.6873 0.2294 94.485 0.014307 1 0 11 0 2 {'cat7'} 8 {'cat7'} 99.828 8 19.682 1 {'cat2'} 0.23938 0.94321 {'cat1'} {'cat8'} 0.9836 0 {'cat2'} {'cat7'} 1 {'cat6'} 7 0.93912 20.137 14 61.349 1.1791 {'cat3'} {'cat2'} 0.60715 51.428 6 0.78977 35.464 0.65368 1 0 0 0 48.458 0 0.97477 14 {'cat3'} 0 79.97 2 {'cat2'} 5 0 NaN 0 {'cat3'} 0.2626 1 0.33319 24.444 {'cat1'} 1 6.4757 {'cat2'} {'cat1'} 98.713 5 0.42147 55.993 0.90185 9.6229 {'cat3'} 0.59941 NaN 1 95.772 5 4.6334 66.465 0.97495 82.876 NaN NaN 34.747 16 0.44732 {'cat5'} 18.94 3 {'cat3'} {'cat2'} 2.0162 {'cat4'} 1 0 0.7369 2.2077 58.755 7 26.399 11 {'cat4'} {'cat7'} 1 0 0 7 {'cat6'} 0.27634 {'cat2'} 0 0.18545 0 0.44395 0.37962 84.268 0 1 {'cat7'} {'cat5'} {'cat1'} {'cat8'} 0.18872 4.2201 1 \n 0.75043 1 {'cat7'} 0 0.72468 {'cat1'} 42.052 0.2122 2.8472 0 0 {'cat8'} 87.489 1 70.066 NaN 0.1777 26.656 1 {'cat6'} 2 NaN {'cat4'} 14 1 0.37585 16 {'cat5'} 6.2102 30.47 0.51333 82.582 9.1449 0.46881 2.2667 39.112 0 0 0.54213 45.993 NaN 0.23409 16 29.893 {'cat6'} 51.955 9 {'cat5'} 12.42 11 9 0.92731 0.85008 88.053 0.22077 NaN 0.18779 1 1 4 0 13 {'cat7'} 3 {'cat7'} 0.57172 13 20.455 1 {'cat7'} 0.44018 0.54848 {'cat8'} {'cat2'} 0.83878 0 {'cat2'} {'cat3'} 1 {'cat1'} 14 0.26177 84.02 15 12.255 30.204 {'cat7'} {'cat1'} 0.1919 12.773 12 0.17839 39.881 0.86984 NaN 1 1 1 27.782 1 0.70802 10 {'cat2'} 1 47.898 14 {'cat7'} 1 0 NaN 0 {'cat3'} 0.46604 0 0.217 94.102 {'cat8'} 0 9.3115 {'cat6'} {'cat2'} 9.8044 3 0.1051 82.827 0.71216 53.709 {'cat5'} 0.23832 NaN 1 34.648 1 46.612 33.513 0.59477 46.427 NaN 15 15.492 1 0.26481 {'cat6'} 22.006 7 {'cat2'} {'cat2'} 24.386 {'cat4'} 4 0 0.25692 23.17 46.064 15 5.8573 9 {'cat2'} {'cat8'} 13 0 0 2 {'cat4'} 0.3198 {'cat1'} 0 0.5316 0 86.334 0.25684 32.098 1 1 {'cat5'} {'cat8'} {'cat3'} {'cat4'} 0.97451 63.557 1 \n 0.14592 4 {'cat7'} 0 0.80837 {'cat1'} 97.619 0.33802 48.059 1 0 {'cat2'} 97.831 0 22.102 1 0.88251 99.865 0 {'cat8'} 10 0 {'cat8'} 10 0 0.24615 10 {'cat7'} 39.309 52.508 0.56512 79.287 3.9656 0.52296 79.515 76.676 1 1 0.20148 20.166 NaN 0.85906 12 21.288 {'cat5'} 7.7557 5 {'cat2'} 58.821 9 3 0.74271 0.6351 50.026 0.49703 NaN NaN 1 1 11 1 3 {'cat3'} 16 {'cat7'} 28.632 15 65.457 10 {'cat8'} 0.65564 0.77294 {'cat1'} {'cat4'} 0.19507 1 {'cat5'} {'cat1'} 1 {'cat3'} 3 0.96177 87.923 1 68.065 73.444 {'cat1'} {'cat7'} 0.29808 25.338 3 0.2132 0.47556 0.4652 NaN 0 1 1 89.164 1 0.5429 13 {'cat1'} 1 38.421 11 {'cat4'} 8 0 NaN 0 {'cat3'} 0.16135 1 0.46393 89.644 {'cat4'} 0 16.766 {'cat4'} {'cat2'} 81.053 8 0.71771 80.779 0.3026 35.302 {'cat7'} 0.57867 NaN 0 11.52 9 30.157 47.529 0.22686 55.045 NaN 3 51.302 7 0.004173 {'cat1'} 4.7262 10 {'cat1'} {'cat3'} 19.527 {'cat2'} 3 1 0.22453 74.564 90.619 16 88.281 15 {'cat3'} {'cat8'} 5 1 1 9 {'cat6'} 0.069406 {'cat5'} 0 0.4729 0 30.63 0.10686 30.801 1 NaN {'cat7'} {'cat5'} {'cat8'} {'cat4'} 0.3273 23.774 1 \n 0.95816 16 {'cat7'} 1 0.44633 {'cat2'} 99.204 0.68164 82.781 0 1 {'cat6'} 78.487 1 70.877 NaN 0.1714 84.239 0 {'cat2'} 8 1 {'cat2'} 14 1 0.58137 10 {'cat4'} 7.3067 16.79 0.52135 7.8103 77.408 0.25653 70.264 62.422 1 0 0.24756 59.604 NaN 0.69711 4 98.4 {'cat4'} 31.759 5 {'cat5'} 57.891 3 10 0.91464 0.8285 70.14 0.052427 79.71 0.83643 0 1 2 0 3 {'cat6'} 4 {'cat8'} 81.151 10 91.666 9 {'cat2'} 0.1391 0.25755 {'cat5'} {'cat6'} 0.76238 1 {'cat4'} {'cat6'} 0 {'cat8'} 11 0.56007 23.028 12 87.984 60.66 {'cat7'} {'cat6'} 0.14919 1.5944 10 0.63361 71.892 0.77893 0 0 1 0 15.344 1 0.22227 11 {'cat8'} 1 71.003 2 {'cat3'} 3 0 58.197 0 {'cat8'} 0.93429 1 0.37861 66.65 {'cat6'} 0 82.393 {'cat4'} {'cat4'} 43.872 11 0.7861 42.282 0.90808 61.523 {'cat3'} 0.87532 NaN 0 7.503 7 78.696 93.422 0.18426 91.233 NaN NaN 35.609 10 0.53128 {'cat6'} 32.148 1 {'cat2'} {'cat4'} 45.434 {'cat7'} 10 0 0.63648 59.404 1.4405 7 94.063 9 {'cat8'} {'cat2'} 15 1 0 8 {'cat2'} 0.88232 {'cat1'} 0 0.58824 1 8.5038 0.41308 64.064 0 0 {'cat6'} {'cat5'} {'cat2'} {'cat6'} 0.45547 46.127 1 \n 0.88215 13 {'cat3'} 0 0.41325 {'cat4'} 5.773 0.1384 33.813 1 0 {'cat2'} 3.8096 1 39.244 1 0.31295 38.056 0 {'cat1'} 9 1 {'cat2'} 4 1 0.0076645 15 {'cat5'} 6.5535 74.857 0.077527 89.953 77.2 0.68302 55.393 13.454 0 1 0.24927 14.79 NaN 0.56575 6 9.3432 {'cat2'} 81.744 16 {'cat7'} 64.407 11 4 0.85217 0.13248 69.501 0.16854 NaN 0.3139 1 0 6 1 3 {'cat4'} 15 {'cat8'} 58.559 7 87.633 5 {'cat4'} 0.39502 0.30638 {'cat2'} {'cat2'} 0.01965 0 {'cat8'} {'cat3'} 0 {'cat7'} 7 0.84049 86.727 13 71.836 88.055 {'cat7'} {'cat8'} 0.27103 23.768 7 0.57172 56.288 0.90873 NaN 1 0 0 8.8601 0 0.43198 8 {'cat2'} 0 99.816 12 {'cat1'} 5 0 NaN 1 {'cat2'} 0.52002 1 0.84524 33.676 {'cat8'} 0 29.468 {'cat4'} {'cat7'} 27.661 5 0.59512 41.886 0.13631 8.6602 {'cat3'} 0.6573 NaN 0 19.106 14 21.871 1.7233 0.58363 9.2326 NaN 13 77.295 15 0.80761 {'cat8'} 22.723 8 {'cat2'} {'cat5'} 47.465 {'cat5'} 16 0 0.43623 86.275 4.4076 11 37.204 6 {'cat3'} {'cat6'} 1 1 0 12 {'cat4'} 0.90492 {'cat1'} 0 0.30842 1 17.829 0.43137 51.718 0 1 {'cat7'} {'cat7'} {'cat4'} {'cat5'} 0.57275 51.453 0 \n\n","truncated":false}}
%---
%[output:9b5b9f21]
% data: {"dataType":"text","outputData":{"text":"\n<strong>fullSet<\/strong>: 130157×181 table\n\nVariables:\n\n <strong>Var1<\/strong>: double\n <strong>Var2<\/strong>: double\n <strong>Var3<\/strong>: cell array of character vectors\n <strong>Var4<\/strong>: double\n <strong>Var5<\/strong>: double\n <strong>Var6<\/strong>: cell array of character vectors\n <strong>Var7<\/strong>: double\n <strong>Var8<\/strong>: double\n <strong>Var9<\/strong>: double\n <strong>Var10<\/strong>: double\n <strong>Var11<\/strong>: double\n <strong>Var12<\/strong>: cell array of character vectors\n <strong>Var13<\/strong>: double\n <strong>Var14<\/strong>: double\n <strong>Var15<\/strong>: double\n <strong>Var16<\/strong>: double\n <strong>Var17<\/strong>: double\n <strong>Var18<\/strong>: double\n <strong>Var19<\/strong>: double\n <strong>Var20<\/strong>: cell array of character vectors\n <strong>Var21<\/strong>: double\n <strong>Var22<\/strong>: double\n <strong>Var23<\/strong>: cell array of character vectors\n <strong>Var24<\/strong>: double\n <strong>Var25<\/strong>: double\n <strong>Var26<\/strong>: double\n <strong>Var27<\/strong>: double\n <strong>Var28<\/strong>: cell array of character vectors\n <strong>Var29<\/strong>: double\n <strong>Var30<\/strong>: double\n <strong>Var31<\/strong>: double\n <strong>Var32<\/strong>: double\n <strong>Var33<\/strong>: double\n <strong>Var34<\/strong>: double\n <strong>Var35<\/strong>: double\n <strong>Var36<\/strong>: double\n <strong>Var37<\/strong>: double\n <strong>Var38<\/strong>: double\n <strong>Var39<\/strong>: double\n <strong>Var40<\/strong>: double\n <strong>Var41<\/strong>: double\n <strong>Var42<\/strong>: double\n <strong>Var43<\/strong>: double\n <strong>Var44<\/strong>: double\n <strong>Var45<\/strong>: cell array of character vectors\n <strong>Var46<\/strong>: double\n <strong>Var47<\/strong>: double\n <strong>Var48<\/strong>: cell array of character vectors\n <strong>Var49<\/strong>: double\n <strong>Var50<\/strong>: double\n <strong>Var51<\/strong>: double\n <strong>Var52<\/strong>: double\n <strong>Var53<\/strong>: double\n <strong>Var54<\/strong>: double\n <strong>Var55<\/strong>: double\n <strong>Var56<\/strong>: double\n <strong>Var57<\/strong>: double\n <strong>Var58<\/strong>: double\n <strong>Var59<\/strong>: double\n <strong>Var60<\/strong>: double\n <strong>Var61<\/strong>: double\n <strong>Var62<\/strong>: double\n <strong>Var63<\/strong>: cell array of character vectors\n <strong>Var64<\/strong>: double\n <strong>Var65<\/strong>: cell array of character vectors\n <strong>Var66<\/strong>: double\n <strong>Var67<\/strong>: double\n <strong>Var68<\/strong>: double\n <strong>Var69<\/strong>: double\n <strong>Var70<\/strong>: cell array of character vectors\n <strong>Var71<\/strong>: double\n <strong>Var72<\/strong>: double\n <strong>Var73<\/strong>: cell array of character vectors\n <strong>Var74<\/strong>: cell array of character vectors\n <strong>Var75<\/strong>: double\n <strong>Var76<\/strong>: double\n <strong>Var77<\/strong>: cell array of character vectors\n <strong>Var78<\/strong>: cell array of character vectors\n <strong>Var79<\/strong>: double\n <strong>Var80<\/strong>: cell array of character vectors\n <strong>Var81<\/strong>: double\n <strong>Var82<\/strong>: double\n <strong>Var83<\/strong>: double\n <strong>Var84<\/strong>: double\n <strong>Var85<\/strong>: double\n <strong>Var86<\/strong>: double\n <strong>Var87<\/strong>: cell array of character vectors\n <strong>Var88<\/strong>: cell array of character vectors\n <strong>Var89<\/strong>: double\n <strong>Var90<\/strong>: double\n <strong>Var91<\/strong>: double\n <strong>Var92<\/strong>: double\n <strong>Var93<\/strong>: double\n <strong>Var94<\/strong>: double\n <strong>Var95<\/strong>: double\n <strong>Var96<\/strong>: double\n <strong>Var97<\/strong>: double\n <strong>Var98<\/strong>: double\n <strong>Var99<\/strong>: double\n <strong>Var100<\/strong>: double\n <strong>Var101<\/strong>: double\n <strong>Var102<\/strong>: double\n <strong>Var103<\/strong>: cell array of character vectors\n <strong>Var104<\/strong>: double\n <strong>Var105<\/strong>: double\n <strong>Var106<\/strong>: double\n <strong>Var107<\/strong>: cell array of character vectors\n <strong>Var108<\/strong>: double\n <strong>Var109<\/strong>: double\n <strong>Var110<\/strong>: double\n <strong>Var111<\/strong>: double\n <strong>Var112<\/strong>: cell array of character vectors\n <strong>Var113<\/strong>: double\n <strong>Var114<\/strong>: double\n <strong>Var115<\/strong>: double\n <strong>Var116<\/strong>: double\n <strong>Var117<\/strong>: cell array of character vectors\n <strong>Var118<\/strong>: double\n <strong>Var119<\/strong>: double\n <strong>Var120<\/strong>: cell array of character vectors\n <strong>Var121<\/strong>: cell array of character vectors\n <strong>Var122<\/strong>: double\n <strong>Var123<\/strong>: double\n <strong>Var124<\/strong>: double\n <strong>Var125<\/strong>: double\n <strong>Var126<\/strong>: double\n <strong>Var127<\/strong>: double\n <strong>Var128<\/strong>: cell array of character vectors\n <strong>Var129<\/strong>: double\n <strong>Var130<\/strong>: double\n <strong>Var131<\/strong>: double\n <strong>Var132<\/strong>: double\n <strong>Var133<\/strong>: double\n <strong>Var134<\/strong>: double\n <strong>Var135<\/strong>: double\n <strong>Var136<\/strong>: double\n <strong>Var137<\/strong>: double\n <strong>Var138<\/strong>: double\n <strong>Var139<\/strong>: double\n <strong>Var140<\/strong>: double\n <strong>Var141<\/strong>: double\n <strong>Var142<\/strong>: double\n <strong>Var143<\/strong>: cell array of character vectors\n <strong>Var144<\/strong>: double\n <strong>Var145<\/strong>: double\n <strong>Var146<\/strong>: cell array of character vectors\n <strong>Var147<\/strong>: cell array of character vectors\n <strong>Var148<\/strong>: double\n <strong>Var149<\/strong>: cell array of character vectors\n <strong>Var150<\/strong>: double\n <strong>Var151<\/strong>: double\n <strong>Var152<\/strong>: double\n <strong>Var153<\/strong>: double\n <strong>Var154<\/strong>: double\n <strong>Var155<\/strong>: double\n <strong>Var156<\/strong>: double\n <strong>Var157<\/strong>: double\n <strong>Var158<\/strong>: cell array of character vectors\n <strong>Var159<\/strong>: cell array of character vectors\n <strong>Var160<\/strong>: double\n <strong>Var161<\/strong>: double\n <strong>Var162<\/strong>: double\n <strong>Var163<\/strong>: double\n <strong>Var164<\/strong>: cell array of character vectors\n <strong>Var165<\/strong>: double\n <strong>Var166<\/strong>: cell array of character vectors\n <strong>Var167<\/strong>: double\n <strong>Var168<\/strong>: double\n <strong>Var169<\/strong>: double\n <strong>Var170<\/strong>: double\n <strong>Var171<\/strong>: double\n <strong>Var172<\/strong>: double\n <strong>Var173<\/strong>: double\n <strong>Var174<\/strong>: double\n <strong>Var175<\/strong>: cell array of character vectors\n <strong>Var176<\/strong>: cell array of character vectors\n <strong>Var177<\/strong>: cell array of character vectors\n <strong>Var178<\/strong>: cell array of character vectors\n <strong>Var179<\/strong>: double\n <strong>Var180<\/strong>: double\n <strong>OutputVar<\/strong>: double\n\nStatistics for applicable variables:\n\n <strong>NumMissing<\/strong> <strong>Min<\/strong> <strong>Median<\/strong> <strong>Max<\/strong> <strong>Mean<\/strong> <strong>Std<\/strong> \n\n <strong>Var1 <\/strong> 0 8.9500e-06 0.4967 3 0.4988 0.2889 \n <strong>Var2 <\/strong> 0 1 9 16 8.5173 4.6030 \n <strong>Var3 <\/strong> 0 \n <strong>Var4 <\/strong> 0 0 0 1 0.4987 0.5000 \n <strong>Var5 <\/strong> 0 1.2800e-05 0.4983 1.0000 0.4991 0.2890 \n <strong>Var6 <\/strong> 0 \n <strong>Var7 <\/strong> 0 9.7900e-05 50.2890 100.0000 50.1943 28.7960 \n <strong>Var8 <\/strong> 0 6.1300e-06 0.5031 1.0000 0.5018 0.2883 \n <strong>Var9 <\/strong> 0 5.5071e-04 49.9007 99.9997 49.8489 28.8067 \n <strong>Var10 <\/strong> 0 0 1 1 0.5010 0.5000 \n <strong>Var11 <\/strong> 0 0 0 1 0.4987 0.5000 \n <strong>Var12 <\/strong> 0 \n <strong>Var13 <\/strong> 0 5.8546e-04 49.7945 99.9999 49.8876 28.8004 \n <strong>Var14 <\/strong> 0 0 1 1 0.5001 0.5000 \n <strong>Var15 <\/strong> 0 5.7452e-04 50.0591 99.9977 50.0887 28.8547 \n <strong>Var16 <\/strong> 64529 0 0 1 0.4984 0.5000 \n <strong>Var17 <\/strong> 0 1.6200e-06 0.4992 1.0000 0.4991 0.2886 \n <strong>Var18 <\/strong> 0 0.0022 50.0056 99.9996 50.0222 28.8370 \n <strong>Var19 <\/strong> 0 0 1 1 0.5004 0.5000 \n <strong>Var20 <\/strong> 0 \n <strong>Var21 <\/strong> 0 1 8 16 8.4924 4.6112 \n <strong>Var22 <\/strong> 64801 0 1 1 0.5024 0.5000 \n <strong>Var23 <\/strong> 0 \n <strong>Var24 <\/strong> 0 1 9 16 8.5071 4.6007 \n <strong>Var25 <\/strong> 0 0 0 1 0.4990 0.5000 \n <strong>Var26 <\/strong> 0 1.8300e-05 0.5016 1.0000 0.5005 0.2885 \n <strong>Var27 <\/strong> 0 1 8 16 8.4931 4.6085 \n <strong>Var28 <\/strong> 0 \n <strong>Var29 <\/strong> 0 4.2868e-04 49.9869 99.9993 49.9801 28.8807 \n <strong>Var30 <\/strong> 0 2.1932e-04 50.0216 99.9991 50.0905 28.8876 \n <strong>Var31 <\/strong> 0 1.0000e-06 0.5015 1.0000 0.5003 0.2891 \n <strong>Var32 <\/strong> 1 5.6967e-04 50.1112 99.9994 50.0189 28.8416 \n <strong>Var33 <\/strong> 0 5.6741e-04 49.9212 99.9993 49.9314 28.8930 \n <strong>Var34 <\/strong> 0 2.2300e-06 0.4992 1.0000 0.4998 0.2886 \n <strong>Var35 <\/strong> 0 3.7100e-05 49.9519 99.9990 49.9928 28.8832 \n <strong>Var36 <\/strong> 0 1.4279e-04 49.9069 99.9997 49.9978 28.9339 \n <strong>Var37 <\/strong> 0 0 0 1 0.4994 0.5000 \n <strong>Var38 <\/strong> 0 0 1 1 0.5025 0.5000 \n <strong>Var39 <\/strong> 0 2.7500e-06 0.5007 1.0000 0.5003 0.2879 \n <strong>Var40 <\/strong> 0 2.7200e-05 49.8749 99.9994 49.9084 28.8732 \n <strong>Var41 <\/strong> 64785 8.0200e-07 0.5006 1.0000 0.5004 0.2891 \n <strong>Var42 <\/strong> 0 5.9600e-06 0.4989 1.0000 0.4994 0.2896 \n <strong>Var43 <\/strong> 0 1 8 16 8.4651 4.6164 \n <strong>Var44 <\/strong> 0 3.1239e-04 49.8623 99.9998 49.9928 28.8865 \n <strong>Var45 <\/strong> 0 \n <strong>Var46 <\/strong> 0 0.0018 50.1330 99.9994 50.0558 28.8727 \n <strong>Var47 <\/strong> 0 1 8 16 8.4903 4.6090 \n <strong>Var48 <\/strong> 0 \n <strong>Var49 <\/strong> 0 0.0019 50.2159 99.9997 50.0737 28.8223 \n <strong>Var50 <\/strong> 0 1 8 16 8.4963 4.6047 \n <strong>Var51 <\/strong> 0 1 9 16 8.5281 4.6044 \n <strong>Var52 <\/strong> 1 1.1100e-06 0.5018 1.0000 0.5014 0.2887 \n <strong>Var53 <\/strong> 0 5.1400e-06 0.5003 1.0000 0.4998 0.2884 \n <strong>Var54 <\/strong> 0 3.0800e-07 50.2487 99.9987 50.1483 28.8295 \n <strong>Var55 <\/strong> 0 1.4300e-06 0.4986 1.0000 0.4997 0.2878 \n <strong>Var56 <\/strong> 65129 5.6683e-04 50.2691 99.9976 50.1857 28.8967 \n <strong>Var57 <\/strong> 64771 1.5600e-06 0.5032 1.0000 0.5013 0.2885 \n <strong>Var58 <\/strong> 0 0 0 1 0.4986 0.5000 \n <strong>Var59 <\/strong> 0 0 1 1 0.5006 0.5000 \n <strong>Var60 <\/strong> 0 1 9 16 8.5011 4.6143 \n <strong>Var61 <\/strong> 0 0 0 1 0.4977 0.5000 \n <strong>Var62 <\/strong> 0 1 9 16 8.4999 4.6106 \n <strong>Var63 <\/strong> 0 \n <strong>Var64 <\/strong> 0 1 9 16 8.5014 4.6088 \n <strong>Var65 <\/strong> 0 \n <strong>Var66 <\/strong> 0 1.2391e-04 50.0074 99.9991 49.9641 28.9498 \n <strong>Var67 <\/strong> 0 1 8 16 8.4859 4.6182 \n <strong>Var68 <\/strong> 1 0.0015 49.8054 99.9994 49.8749 28.8767 \n <strong>Var69 <\/strong> 0 1 8 16 8.4777 4.6035 \n <strong>Var70 <\/strong> 0 \n <strong>Var71 <\/strong> 0 1.4900e-06 0.5021 1.0000 0.5006 0.2883 \n <strong>Var72 <\/strong> 0 9.4100e-07 0.4996 1.0000 0.4999 0.2891 \n <strong>Var73 <\/strong> 0 \n <strong>Var74 <\/strong> 0 \n <strong>Var75 <\/strong> 0 5.3000e-06 0.5006 1.0000 0.5000 0.2884 \n <strong>Var76 <\/strong> 0 0 0 1 0.4999 0.5000 \n <strong>Var77 <\/strong> 0 \n <strong>Var78 <\/strong> 0 \n <strong>Var79 <\/strong> 0 0 0 1 0.4989 0.5000 \n <strong>Var80 <\/strong> 0 \n <strong>Var81 <\/strong> 0 1 8 16 8.4915 4.6166 \n <strong>Var82 <\/strong> 0 3.2300e-06 0.4983 1.0000 0.4990 0.2888 \n <strong>Var83 <\/strong> 0 0.0013 49.9537 99.9999 49.9872 28.8875 \n <strong>Var84 <\/strong> 0 1 8 16 8.4849 4.6167 \n <strong>Var85 <\/strong> 0 3.7851e-04 50.1713 100.0000 50.1058 28.8516 \n <strong>Var86 <\/strong> 0 8.6883e-04 50.0117 99.9994 49.9820 28.8485 \n <strong>Var87 <\/strong> 0 \n <strong>Var88 <\/strong> 0 \n <strong>Var89 <\/strong> 0 2.9000e-06 0.5017 1.0000 0.5013 0.2888 \n <strong>Var90 <\/strong> 0 0.0011 50.1133 99.9985 50.0067 28.8288 \n <strong>Var91 <\/strong> 0 1 8 16 8.4952 4.6054 \n <strong>Var92 <\/strong> 0 5.4000e-06 0.5018 1.0000 0.5014 0.2885 \n <strong>Var93 <\/strong> 0 5.1118e-04 49.8266 99.9979 49.9093 28.8811 \n <strong>Var94 <\/strong> 64763 1.0000e-05 0.4958 1.0000 0.4984 0.2895 \n <strong>Var95 <\/strong> 64998 0 1 1 0.5031 0.5000 \n <strong>Var96 <\/strong> 0 0 1 1 0.5011 0.5000 \n <strong>Var97 <\/strong> 0 0 1 1 0.5007 0.5000 \n <strong>Var98 <\/strong> 0 0 0 1 0.4980 0.5000 \n <strong>Var99 <\/strong> 0 3.4941e-04 49.8407 99.9994 49.9421 28.8763 \n <strong>Var100 <\/strong> 0 0 1 1 0.5002 0.5000 \n <strong>Var101 <\/strong> 0 3.6300e-06 0.5005 1.0000 0.5003 0.2885 \n <strong>Var102 <\/strong> 0 1 9 16 8.4933 4.6169 \n <strong>Var103 <\/strong> 0 \n <strong>Var104 <\/strong> 0 0 1 1 0.5015 0.5000 \n <strong>Var105 <\/strong> 0 6.0936e-04 49.9088 99.9993 49.9827 28.7952 \n <strong>Var106 <\/strong> 0 1 8 16 8.4849 4.6065 \n <strong>Var107 <\/strong> 0 \n <strong>Var108 <\/strong> 0 1 9 16 8.5216 4.6118 \n <strong>Var109 <\/strong> 0 0 1 1 0.5013 0.5000 \n <strong>Var110 <\/strong> 65162 0.0011 49.9102 99.9986 49.9863 28.9072 \n <strong>Var111 <\/strong> 0 0 1 1 0.5004 0.5000 \n <strong>Var112 <\/strong> 0 \n <strong>Var113 <\/strong> 0 1.5900e-06 0.4984 1.0000 0.4988 0.2892 \n <strong>Var114 <\/strong> 0 0 0 1 0.4997 0.5000 \n <strong>Var115 <\/strong> 0 4.5800e-06 0.5019 1.0000 0.5011 0.2891 \n <strong>Var116 <\/strong> 0 7.2261e-04 49.8834 99.9965 49.9626 28.8326 \n <strong>Var117 <\/strong> 0 \n <strong>Var118 <\/strong> 0 0 1 1 0.5000 0.5000 \n <strong>Var119 <\/strong> 0 0.0010 50.0893 99.9989 50.0965 28.8539 \n <strong>Var120 <\/strong> 0 \n <strong>Var121 <\/strong> 0 \n <strong>Var122 <\/strong> 0 4.6656e-04 50.1437 100.0000 50.0765 28.8553 \n <strong>Var123 <\/strong> 0 1 9 16 8.5056 4.6164 \n <strong>Var124 <\/strong> 0 2.6100e-07 0.5001 1.0000 0.5008 0.2890 \n <strong>Var125 <\/strong> 0 1.8114e-04 50.0318 99.9978 49.9850 28.8123 \n <strong>Var126 <\/strong> 0 8.1100e-06 0.5001 1.0000 0.5007 0.2884 \n <strong>Var127 <\/strong> 0 0.0015 50.0353 99.9993 49.9632 28.8498 \n <strong>Var128 <\/strong> 0 \n <strong>Var129 <\/strong> 0 1.6800e-05 0.5032 1.0000 0.5016 0.2888 \n <strong>Var130 <\/strong> 65085 2.7900e-05 0.5007 1.0000 0.5005 0.2879 \n <strong>Var131 <\/strong> 0 0 1 1 0.5002 0.5000 \n <strong>Var132 <\/strong> 1 3.4322e-04 49.7605 99.9994 49.8648 28.9040 \n <strong>Var133 <\/strong> 0 1 8 16 8.4852 4.6053 \n <strong>Var134 <\/strong> 0 4.6608e-04 49.6660 99.9997 49.8328 28.8649 \n <strong>Var135 <\/strong> 0 0.0020 49.9823 99.9996 50.0411 28.8907 \n <strong>Var136 <\/strong> 0 1.1900e-06 0.4995 1.0000 0.4999 0.2887 \n <strong>Var137 <\/strong> 0 5.3669e-04 49.8277 99.9992 49.9715 28.8374 \n <strong>Var138 <\/strong> 65193 0 0 1 0.4996 0.5000 \n <strong>Var139 <\/strong> 64811 1 8 16 8.5180 4.5953 \n <strong>Var140 <\/strong> 0 7.9700e-05 49.8816 99.9991 49.8808 28.8979 \n <strong>Var141 <\/strong> 0 1 9 16 8.5183 4.6205 \n <strong>Var142 <\/strong> 0 6.7900e-06 0.4996 1.0000 0.4997 0.2887 \n <strong>Var143 <\/strong> 0 \n <strong>Var144 <\/strong> 0 5.7149e-04 50.1066 99.9986 50.0847 28.8919 \n <strong>Var145 <\/strong> 0 1 8 16 8.4851 4.6189 \n <strong>Var146 <\/strong> 0 \n <strong>Var147 <\/strong> 0 \n <strong>Var148 <\/strong> 0 0.0030 50.0885 100.0000 50.0766 28.9298 \n <strong>Var149 <\/strong> 0 \n <strong>Var150 <\/strong> 0 1 9 16 8.5134 4.6053 \n <strong>Var151 <\/strong> 0 0 1 1 0.5007 0.5000 \n <strong>Var152 <\/strong> 0 2.6500e-05 0.5001 1.0000 0.4999 0.2890 \n <strong>Var153 <\/strong> 0 0.0011 50.1574 99.9993 50.1179 28.8518 \n <strong>Var154 <\/strong> 0 2.9284e-04 50.1308 99.9979 50.0154 28.8967 \n <strong>Var155 <\/strong> 0 1 9 16 8.4959 4.6156 \n <strong>Var156 <\/strong> 0 4.2600e-05 49.6953 99.9982 49.8742 28.8786 \n <strong>Var157 <\/strong> 0 1 8 16 8.4713 4.6145 \n <strong>Var158 <\/strong> 0 \n <strong>Var159 <\/strong> 0 \n <strong>Var160 <\/strong> 0 1 9 16 8.5157 4.6186 \n <strong>Var161 <\/strong> 0 0 1 1 0.5005 0.5000 \n <strong>Var162 <\/strong> 0 0 1 1 0.5005 0.5000 \n <strong>Var163 <\/strong> 0 1 8 16 8.4779 4.6098 \n <strong>Var164 <\/strong> 0 \n <strong>Var165 <\/strong> 0 1.9900e-06 0.5014 1.0000 0.5008 0.2889 \n <strong>Var166 <\/strong> 0 \n <strong>Var167 <\/strong> 0 0 1 1 0.5002 0.5000 \n <strong>Var168 <\/strong> 0 6.9300e-06 0.5001 1.0000 0.4999 0.2894 \n <strong>Var169 <\/strong> 0 0 0 1 0.4995 0.5000 \n <strong>Var170 <\/strong> 0 0.0033 49.7728 99.9998 49.9018 28.9378 \n <strong>Var171 <\/strong> 0 5.8100e-06 0.4974 1.0000 0.4990 0.2885 \n <strong>Var172 <\/strong> 0 6.1200e-05 49.8134 99.9997 49.9008 28.8748 \n <strong>Var173 <\/strong> 0 0 0 1 0.4998 0.5000 \n <strong>Var174 <\/strong> 65146 0 0 1 0.4978 0.5000 \n <strong>Var175 <\/strong> 0 \n <strong>Var176 <\/strong> 0 \n <strong>Var177 <\/strong> 0 \n <strong>Var178 <\/strong> 0 \n <strong>Var179 <\/strong> 0 7.4100e-06 0.5032 1.0000 0.5020 0.2890 \n <strong>Var180 <\/strong> 0 4.4462e-04 49.9503 99.9989 49.9980 28.9363 \n <strong>OutputVar<\/strong> 0 0 0 1 0.4988 0.5000 \n\n","truncated":false}}
%---
%[output:766f1a15]
% data: {"dataType":"tabular","outputData":{"columnNames":["Var1","Var2","Var4","Var5","Var7","Var8","Var9","Var10","Var11","Var13","Var14","Var15","Var16","Var17","Var18","Var19","Var21","Var22","Var24","Var25","Var26","Var27","Var29","Var30","Var31","Var32","Var33","Var34","Var35","Var36"],"columns":144,"dataTypes":["double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double"],"header":"130157×144 table","name":"fullSet","rows":130157,"type":"table","value":[["0.5279","12","0","0.4878","83.1020","0.3631","89.4572","0","0","16.5682","1","41.6474","NaN","0.0390","7.9056","0","7","1","2","1","0.6328","8","62.3182","60.9407","0.8167","96.9343","84.7736","0.4304","35.6369","63.7412"],["0.6499","2","0","0.6358","48.1167","0.6457","17.9295","0","0","36.9782","1","67.6545","NaN","0.2555","80.4210","1","14","NaN","6","1","0.4195","1","4.8285","44.5476","0.1636","72.4476","87.3820","0.9523","39.5861","47.9419"],["0.9321","16","0","0.9962","58.1876","0.9837","67.8675","0","0","10.9023","1","85.2034","0","0.0452","9.0337","1","2","1","7","1","0.9491","8","2.8030","28.3008","0.5898","83.3074","97.9881","0.3527","87.2203","5.8101"],["0.0174","14","0","0.7430","29.0995","0.1845","47.6854","1","1","3.5347","0","18.5355","1","0.6656","33.7669","1","9","1","15","0","0.1065","15","19.8782","57.7560","0.8345","22.7350","16.8806","0.8276","23.7773","11.2337"],["0.7504","1","0","0.7247","42.0515","0.2122","2.8472","0","0","87.4885","1","70.0657","NaN","0.1777","26.6555","1","2","NaN","14","1","0.3758","16","6.2102","30.4700","0.5133","82.5817","9.1449","0.4688","2.2667","39.1118"],["0.1459","4","0","0.8084","97.6191","0.3380","48.0588","1","0","97.8312","0","22.1019","1","0.8825","99.8652","0","10","0","10","0","0.2462","10","39.3087","52.5083","0.5651","79.2867","3.9656","0.5230","79.5147","76.6762"],["0.9582","16","1","0.4463","99.2038","0.6816","82.7815","0","1","78.4872","1","70.8772","NaN","0.1714","84.2394","0","8","1","14","1","0.5814","10","7.3067","16.7904","0.5213","7.8103","77.4084","0.2565","70.2639","62.4216"],["0.8821","13","0","0.4133","5.7730","0.1384","33.8129","1","0","3.8096","1","39.2435","1","0.3130","38.0561","0","9","1","4","1","0.0077","15","6.5535","74.8568","0.0775","89.9532","77.2001","0.6830","55.3929","13.4536"],["0.3262","2","1","0.1440","48.1407","0.6594","51.3461","0","0","26.0766","0","82.3164","1","0.3362","87.7472","0","7","1","11","0","0.2794","8","60.9586","34.5162","0.4302","0.9819","22.9999","0.6689","97.1114","88.8247"],["0.4925","6","1","0.9276","19.2221","0.1898","85.3821","0","0","99.1767","0","23.8219","0","0.8143","76.8590","1","5","0","14","1","0.3269","13","29.0209","28.9163","0.2623","48.8367","34.6445","0.2757","85.2989","17.4453"],["0.6171","15","1","0.2454","70.6910","0.5722","87.7434","0","0","21.3921","1","6.9251","0","0.3126","87.2012","0","10","NaN","15","0","0.3042","1","66.2014","0.3256","0.1936","86.3299","52.8578","0.4715","32.2597","48.7285"],["0.6861","13","0","0.9342","35.0732","0.9680","26.9309","1","1","1.0936","0","78.6606","NaN","0.8803","84.7315","0","3","1","14","1","0.7919","15","38.5386","29.0773","0.1591","50.0532","18.9111","0.7856","94.6287","95.9238"],["0.5790","13","1","0.7723","54.0273","0.2682","54.3598","1","1","3.9039","1","26.3837","0","0.6541","78.3183","1","11","NaN","4","1","0.4522","10","87.9933","3.5686","0.5705","6.1430","99.0711","0.9379","12.9953","70.5101"],["0.6077","14","0","0.9699","83.0992","0.8433","8.3484","1","1","46.5667","1","69.2019","NaN","0.9204","53.7289","1","7","NaN","4","0","0.4786","2","78.2407","81.5174","0.5082","33.3540","27.9727","0.3936","12.2495","59.8221"]]}}
%---
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%---
%[output:4fd33038]
% data: {"dataType":"tabular","outputData":{"columnNames":["Var1","Var2","Var4","Var5","Var7","Var8","Var9","Var10","Var11","Var13","Var14","Var15","Var16","Var17","Var18","Var19","Var21","Var22","Var24","Var25","Var26","Var27","Var29","Var30","Var31","Var32","Var33","Var34","Var35","Var36"],"columns":144,"dataTypes":["double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double","double"],"header":"26734×144 table","name":"cleanedSet","rows":26734,"type":"table","value":[["0.6861","13","0","0.9342","35.0732","0.9680","26.9309","1","1","1.0936","0","78.6606","NaN","0.8803","84.7315","0","3","1","14","1","0.7919","15","38.5386","29.0773","0.1591","50.0532","18.9111","0.7856","94.6287","95.9238"],["0.4070","16","0","0.7819","28.4556","0.4984","73.2836","0","1","82.6994","1","59.2935","NaN","0.1478","4.1883","0","14","NaN","1","0","0.3360","10","29.0936","95.3539","0.0499","78.6941","32.8584","0.6135","95.5962","38.8750"],["0.8394","6","0","0.9533","68.8778","0.9177","39.6072","0","0","57.9324","1","8.6084","NaN","0.3964","56.1317","0","6","0","2","1","0.2037","16","16.2950","34.2564","0.2992","22.0904","52.1431","0.4218","34.9090","75.8100"],["0.4992","7","1","0.1001","80.8049","0.7768","25.8267","1","0","53.1113","1","61.7935","0","0.1426","14.3823","1","16","NaN","2","1","0.8842","14","83.6509","5.9216","0.1762","8.4068","39.6502","0.1692","30.6751","98.6158"],["0.3390","3","0","0.5487","2.3142","0.7301","45.6748","1","0","3.0511","0","65.6005","NaN","0.6323","65.5046","1","8","1","11","1","0.1030","2","65.1977","56.1891","0.8427","32.0179","98.6448","0.6444","56.9519","97.5540"],["0.1573","15","0","0.8639","24.4174","0.5556","98.4169","0","1","86.9452","0","96.6083","NaN","0.1010","98.4909","1","11","0","3","0","0.0978","10","18.7763","69.9077","0.1552","81.1178","48.9258","0.8167","19.7195","26.3423"],["0.4618","9","0","0.6888","86.7410","0.9191","43.9109","1","1","10.4278","1","30.6892","1","0.5889","10.5680","1","12","1","11","0","0.1537","6","13.0784","23.9750","0.0912","14.1828","83.6325","0.0265","57.4966","38.0457"],["0.6613","5","0","0.5480","51.1369","0.8358","21.4470","0","0","91.2312","1","22.4238","NaN","0.3039","44.2917","1","9","0","7","0","0.1906","5","82.2909","9.2166","0.7664","2.7598","14.3960","0.1255","38.3621","79.4547"],["0.2610","7","1","0.2429","17.3395","0.4853","38.4363","0","1","37.6305","1","62.9506","1","0.5660","68.7863","1","9","NaN","10","0","0.1933","11","47.6312","46.4959","0.7538","17.7227","51.9964","0.2529","76.9382","22.6337"],["0.4882","16","1","0.3084","37.9997","0.4012","56.7035","0","0","75.4122","0","74.3050","NaN","0.7226","92.7491","1","5","NaN","1","0","0.6007","7","49.4472","69.0385","0.7701","75.8226","91.4350","0.6183","90.2700","95.3796"],["0.0661","12","1","0.8652","78.6405","0.5912","19.0513","0","1","3.5409","1","47.0239","1","0.9586","44.6288","1","5","NaN","13","1","0.7756","15","44.6869","87.9545","0.4069","72.2703","62.5499","0.3119","16.6172","91.2037"],["0.7346","9","1","0.1804","50.5069","0.3554","53.0151","1","0","89.3403","0","80.4660","1","0.8714","35.3659","0","14","1","4","0","0.0299","11","22.7043","61.2023","0.7761","30.2936","71.1626","0.8586","23.9766","21.2123"],["0.4646","6","1","0.5710","59.6717","0.1719","85.7521","1","0","8.5122","1","56.4273","NaN","0.0809","76.0816","1","12","0","9","0","0.9306","11","58.3916","42.0275","0.3473","45.3439","79.6447","0.9455","4.0118","41.2283"],["0.4758","12","0","0.9042","55.9338","0.8438","33.7995","1","0","41.9959","0","18.8815","1","0.9840","6.3169","1","12","NaN","12","0","0.2842","10","39.5676","45.0182","0.0298","65.6685","18.2712","0.9275","41.6097","71.2025"]]}}
%---
%[output:04359215]
% data: {"dataType":"warning","outputData":{"text":"Warning: Predictor matrix contains NaNs. Remove the NaNs to reduce memory consumption and speed up training."}}
%---
%[output:10c4f4f9]
% data: 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","height":420,"width":560}}
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