plot

Train a classification model and create a shapley object. Then plot the Shapley values by using the object function plot.

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 prediction for the last observation. For faster computation, shapley subsamples 100 observations from the predictor data in tbl to compute the Shapley values.

queryPoint = tbl(end,:)

queryPoint=1×8 table
     ID      WC_TA    RE_TA    EBIT_TA    MVE_BVTD    S_TA    Industry    Rating
    _____    _____    _____    _______    ________    ____    ________    ______

    73104    0.239    0.463     0.065      2.924      0.34       2        {'AA'}

explainer = shapley(blackbox,tbl,QueryPoints=queryPoint);

For a classification model, shapley computes Shapley values using the predicted class score for each class. Display the values in the Shapley property.

explainer.Shapley

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

    "WC_TA"        0.061172      0.023988     0.0085073    -0.0019268       -0.03895     -0.056012     -0.051658
    "RE_TA"         0.16878      0.089521      0.048741     -0.021252       -0.10389      -0.22968      -0.30796
    "EBIT_TA"     0.0013159    0.00051165    0.00039115    1.1425e-05    -0.00090913    -0.0016812    -0.0014235
    "MVE_BVTD"        1.351         1.271       0.51796      -0.27612       -0.86555       -1.0915       -0.8458
    "S_TA"        -0.012304    -0.0083217    0.00019836    -0.0026384     -2.257e-05     0.0017866    -0.0026664
    "Industry"     -0.11427     -0.053759     0.0058104      0.090519        0.11176       0.13811       0.18671

The Shapley property contains the Shapley values of all features for each class.

Plot the Shapley values for the predicted class by using the plot function.

plot(explainer)

The horizontal bar graph shows the Shapley values for all variables, sorted by their absolute values. Each Shapley value explains the deviation of the score for the query point from the average score of the predicted class, due to the corresponding variable.

Plot the Shapley values for all classes by specifying all class names in explainer.BlackboxModel.

plot(explainer,ClassNames=explainer.BlackboxModel.ClassNames)

Train a regression model and create a shapley object. Use the object function fit to compute the Shapley values for the specified query point. Then plot the Shapley values of the predictors by using the object function plot. Specify the number of important predictors to plot when you call the plot 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 can help 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 of all predictor variables for the first observation in tbl. To speed up computations, the fit object function uses the sampled observations rather than all of X to compute the Shapley values.

queryPoint = tbl(1,:)

queryPoint=1×7 table
    Acceleration    Cylinders    Displacement    Horsepower    Model_Year    Weight    MPG
    ____________    _________    ____________    __________    __________    ______    ___

         12             8            307            130            70         3504     18 

explainer = fit(explainer,queryPoint);

For a regression model, fit computes Shapley values using the predicted response, and stores them in the Shapley property of the shapley object. Display the values in the Shapley property.

explainer.Shapley

ans=6×2 table
      Predictor        Value  
    ______________    ________

    "Acceleration"    -0.33821
    "Cylinders"       -0.97631
    "Displacement"     -1.1425
    "Horsepower"      -0.62927
    "Model_Year"      -0.17268
    "Weight"          -0.87595

Plot the Shapley values for the query point by using the plot function. Specify to plot only the five most important predictors for the predicted response.

plot(explainer,NumImportantPredictors=5)

The horizontal bar graph shows the Shapley values for the five most important predictors, sorted by their absolute values. Each Shapley value explains the deviation of the prediction for the query point from the average, due to the corresponding variable.

Train a classification model and create a shapley object. Plot the mean absolute Shapley values for multiple query points by using the plot object function. Then plot the Shapley values for one of the query points.

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, the fit function computes Shapley values using the predicted class score for each class. When you specify multiple query points, the function computes the mean absolute Shapley value for each predictor and each class, across all query points.

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 example, the explainer.MeanAbsoluteShapley.AAA(1) value is the average of the absolute Shapley values for the WC_TA predictor and the AAA class, across all observations in queryPoints.

explainer.MeanAbsoluteShapley.AAA(1)

ans = 
0.0560

Plot the mean absolute Shapley values by using the plot object function.

plot(explainer)

For each class, the MVE_BVTD predictor has the greatest mean absolute Shapley value.

Select the first query point and determine the class prediction for the query point.

queryPoint = explainer.QueryPoints(1,:)

queryPoint=1×6 table
    WC_TA    RE_TA    EBIT_TA    MVE_BVTD    S_TA     Industry
    _____    _____    _______    ________    _____    ________

    0.197    0.471     0.067      2.304      0.602       1    

queryPointPrediction = explainer.BlackboxFitted(1)

queryPointPrediction = 1×1 cell array
    {'A'}

Plot the Shapley values for the query point by using the QueryPointIndices name-value argument. Change the color of the bars to match the color of the query point predicted class (A).

b = plot(explainer,QueryPointIndices=1);
b.FaceColor = [0.9290 0.6940 0.1250];

For this query point, the MVE_BVTD predictor explains the largest deviation of the class A predicted score from the average.