boxchart

Train a regression model and create a shapley object. Use the fit object function to compute the Shapley values for the specified query points. Then visualize the Shapley values for multiple query points by using the boxchart object function.

Load the carbig data set, which contains measurements of cars made in the 1970s and early 1980s.

load carbig

Create a table containing the predictor variables Acceleration, Cylinders, and so on, as well as the response variable MPG.

tbl = table(Acceleration,Cylinders,Displacement, ...
    Horsepower,Model_Year,Weight,MPG);

Removing missing values in a training set helps to reduce memory consumption and speed up training for the fitrkernel function. Remove missing values in tbl.

tbl = rmmissing(tbl);

Train a blackbox model of MPG by using the fitrkernel function. Specify the Cylinders and Model_Year variables as categorical predictors. Standardize the remaining predictors.

rng("default") % For reproducibility
mdl = fitrkernel(tbl,"MPG",CategoricalPredictors=[2 5], ...
    Standardize=true);

Create a shapley object. Because mdl does not contain training data, specify the data set tbl.

explainer = shapley(mdl,tbl)

explainer = 
            BlackboxModel: [1×1 RegressionKernel]
              QueryPoints: []
           BlackboxFitted: []
                  Shapley: []
                        X: [392×7 table]
    CategoricalPredictors: [2 5]
                   Method: "interventional-kernel"
                Intercept: 23.2474
               NumSubsets: 64

explainer stores the training data tbl in the X property. By default, shapley subsamples 100 observations from the data in X, and stores their indices in the SampledObservationIndices property.

Compute the Shapley values for all observations in tbl. To speed up computations, the fit object function uses the sampled observations instead of all of X to compute the Shapley values. If you have a Parallel Computing Toolbox™ license, you can further reduce computational time by setting the UseParallel name-value argument.

explainer = fit(explainer,tbl,UseParallel=true)

explainer = 
shapley explainer with the following mean absolute Shapley values:

      Predictor        Value 
    ______________    _______

    "Acceleration"     0.5678
    "Cylinders"       0.96799
    "Displacement"    0.79668
    "Horsepower"      0.78681
    "Model_Year"      0.86258
    "Weight"            0.987


  Properties, Methods

For a regression model, fit computes Shapley values using the predicted response, and stores them in the Shapley property of the shapley object. Because explainer contains Shapley values for multiple query points, the function displays the mean absolute Shapley values by default.

Visualize the distribution of the Shapley values by using the boxchart object function.

boxchart(explainer)

For each predictor, the function displays a box plot of the Shapley values for the query points. The function determines the order of the predictors by using the mean absolute Shapley values.

The box plot for the Weight predictor indicates that the Shapley values are distributed symmetrically about the median. The minimum is slightly less than –2, the 25th percentile is approximately –1, the median is approximately 0, the 75th percentile is approximately 1, and the maximum is approximately 2.

Use a data tip to view the Shapley value metrics for the Weight predictor.

b = boxchart(explainer);
datatip(b,"DataIndex",6);

Train a classification model and create a shapley object. Then visualize the Shapley values for multiple query points by using the boxchart object function.

Load the CreditRating_Historical data set. The data set contains customer IDs and their financial ratios, industry labels, and credit ratings.

tbl = readtable("CreditRating_Historical.dat");

Display the first three rows of the table.

head(tbl,3)

     ID      WC_TA    RE_TA    EBIT_TA    MVE_BVTD    S_TA     Industry    Rating
    _____    _____    _____    _______    ________    _____    ________    ______

    62394    0.013    0.104     0.036      0.447      0.142       3        {'BB'}
    48608    0.232    0.335     0.062      1.969      0.281       8        {'A' }
    42444    0.311    0.367     0.074      1.935      0.366       1        {'A' }

Train a blackbox model of credit ratings by using the fitcecoc function. Use the variables from the second through seventh columns in tbl as the predictor variables. A recommended practice is to specify the class names to set the order of the classes.

blackbox = fitcecoc(tbl,"Rating", ...
    PredictorNames=tbl.Properties.VariableNames(2:7), ...
    CategoricalPredictors="Industry", ...
    ClassNames={'AAA','AA','A','BBB','BB','B','CCC'});

Create a shapley object that explains the predictions for multiple query points. For faster computation, shapley subsamples 100 observations from the predictor data in blackbox to compute the Shapley values. Specify the sampled observations as the query points in the call to the fit object function.

rng("default") % For reproducibility
explainer = shapley(blackbox);
queryPoints = explainer.X(explainer.SampledObservationIndices,:);
explainer = fit(explainer,queryPoints);

For a classification model, fit computes Shapley values using the predicted class scores, and stores them in the Shapley property of the shapley object. Because explainer contains Shapley values for multiple query points, display the mean absolute Shapley values instead.

explainer.MeanAbsoluteShapley

ans=6×8 table
    Predictor        AAA           AA            A           BBB          BB            B           CCC   
    __________    _________    __________    _________    _________    _________    _________    _________

    "WC_TA"        0.055977      0.034453     0.027338     0.023902     0.036098     0.054763     0.054931
    "RE_TA"         0.12468       0.10314      0.10787     0.087013     0.090298      0.17123       0.2552
    "EBIT_TA"     0.0015598    0.00095166    0.0011936    0.0010499    0.0010047    0.0018817    0.0017712
    "MVE_BVTD"      0.84966       0.68785      0.66198      0.94501       1.3672       1.5715       1.2161
    "S_TA"         0.025009     0.0095605     0.010606     0.014469    0.0017235    0.0075275     0.012529
    "Industry"     0.076169      0.085926     0.063854     0.046528     0.053801      0.11261      0.11829

For each predictor and class, the mean absolute Shapley value is the absolute value of the Shapley values, averaged across all query points. For class AA, the MVE_BVTD predictor has a noticeably greater mean absolute Shapley value than the other predictors.

Visualize the distribution of the Shapley values for class AA by using the boxchart object function.

boxchart(explainer,ClassName={'AA'})

For each predictor, the function displays a box plot of the Shapley values for the query points. The function determines the order of the predictors by using the mean absolute Shapley values.

For class AA, some of the Shapley values for the RE_TA predictor are outliers. This result suggests that, for a few query points, the predictor greatly affects the class AA predicted score.