plotDependence

Train a classification model and create a shapley object. Use the fit object function to compute the Shapley values for the specified query points. Then for each predictor, visualize the dependence of the Shapley values on the predictor values by using the plotDependence 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);

Visualize the Shapley values for a specified predictor by using the plotDependence object function.

predictor = "MVE_BVTD";
plotDependence(explainer,predictor)

By default, the function shows the Shapley values for the first class, AAA. For noncategorical predictors, the function displays a scatter plot, where the x-axis corresponds to the predictor values and the y-axis corresponds to the Shapley values for the predictor.

For class AAA, the Shapley values for the MVE_BVTD predictor tend to increase as the predictor values increase from 0 to 4. For MVE_BVTD values greater than 4, the corresponding Shapley values tend to remain constant (between 1.5 and 2).

For categorical predictors, plotDependence displays box plots for each category in the categorical predictor. The function determines categorical predictors based on the CategoricalPredictors property of the shapley object.

Visualize the Shapley values for the categorical predictor Industry. Specify the class.

class = "A";
plotDependence(explainer,"Industry",ClassName=class)

For class A, the distribution of the Shapley values varies across different industries. For example, industry 3 has exclusively positive Shapley values, whereas industry 9 has exclusively negative Shapley values.

Train a regression model and create a shapley object using multiple query points. Then for each predictor, visualize the dependence of the Shapley values on the predictor values. Use color to see the dependence on a second predictor.

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.

mdl = fitrkernel(tbl,"MPG",CategoricalPredictors=[2 5], ...
    Standardize=true);

Create a shapley object that explains the predictions for multiple query points. Because mdl does not contain training data, specify to compute Shapley values using the predictor data in tbl. For faster computation, specify to subsample 200 observations from tbl. Use all observations in tbl as query points.

explainer = shapley(mdl,tbl,NumObservationsToSample=200, ...
    QueryPoints=tbl);

Visualize the Shapley values for a specific predictor by using the plotDependence object function. Use color to display a second predictor. Note that if you want to specify a color predictor, the x-axis predictor must be a noncategorical predictor.

predictor = "Weight";
colorPredictor = "Horsepower";
plotDependence(explainer,predictor,ColorPredictor=colorPredictor)

For Weight values between 2000 and 4000, the corresponding Shapley values tend to decrease as the Weight values increase. Based on the color of the points in the plot, Horsepower values tend to increase as Weight values increase.