shapley

Shapley values

Since R2021a

Description

The Shapley value of a feature for a query point explains the deviation of the prediction for the query point from the average prediction, due to the feature. For each query point, the sum of the Shapley values for all features corresponds to the total deviation of the prediction from the average.

You can create a shapley object for a machine learning model with a specified query point or query points (queryPoints). The software creates an object and computes the Shapley values of all features for the query points.

Use the Shapley values to explain the contribution of individual features to a prediction at each specified query point. Use the plot function to display a bar graph of the Shapley values for one query point or the mean absolute Shapley values averaged across multiple query points. If you have multiple query points, you can use the boxchart, plotDependence, and swarmchart functions to visualize Shapley values. You can compute the Shapley values for new query points by using the fit function.

Creation

Syntax

explainer = shapley(blackbox)

explainer = shapley(blackbox,X)

explainer = shapley(___,QueryPoints=queryPoints)

explainer = shapley(___,Name=Value)

Description

explainer = shapley(blackbox) creates the shapley object explainer using the machine learning model object blackbox, which contains predictor data. To compute Shapley values, use the fit function with explainer.

explainer = shapley(blackbox,X) creates a shapley object using the predictor data in X.

explainer = shapley(___,QueryPoints=queryPoints) also computes the Shapley values for the query points queryPoints and stores the computed Shapley values in the Shapley property of explainer. You can specify queryPoints in addition to any of the input argument combinations in the previous syntaxes.

explainer = shapley(___,Name=Value) specifies additional options using one or more name-value arguments. For example, specify UseParallel=true to compute Shapley values in parallel.

Input Arguments

blackbox — Machine learning model to be interpreted

regression model object | classification model object | function handle

Machine learning model to be interpreted, specified as a full or compact regression or classification model object or a function handle.

• Full or compact model object — You can specify a full or compact regression or classification model object, which has a predict object function. The software uses the predict function to compute Shapley values.

  • If you specify a model object that does not contain predictor data (for example, a compact model), then you must provide the predictor data using X.

  • When you train a model, use a numeric matrix or table for the predictor data where rows correspond to individual observations.

  • shapley does not support a model object trained with more than one response variable.

  Regression Model Object

  Supported Model	Full or Compact Regression Model Object
  Ensemble of regression models	RegressionEnsemble, RegressionBaggedEnsemble, CompactRegressionEnsemble
  Gaussian kernel regression model using random feature expansion	RegressionKernel
  Gaussian process regression	RegressionGP, CompactRegressionGP
  Generalized additive model	RegressionGAM, CompactRegressionGAM
  Linear regression for high-dimensional data	RegressionLinear
  Neural network regression model	RegressionNeuralNetwork, CompactRegressionNeuralNetwork
  Regression tree	RegressionTree, CompactRegressionTree
  Support vector machine regression	RegressionSVM, CompactRegressionSVM

  Classification Model Object

  Supported Model	Full or Compact Classification Model Object
  Discriminant analysis classifier	ClassificationDiscriminant, CompactClassificationDiscriminant
  Multiclass model for support vector machines or other classifiers	ClassificationECOC, CompactClassificationECOC
  Ensemble of learners for classification	ClassificationEnsemble, CompactClassificationEnsemble, ClassificationBaggedEnsemble
  Gaussian kernel classification model using random feature expansion	ClassificationKernel
  Generalized additive model	ClassificationGAM, CompactClassificationGAM
  k-nearest neighbor classifier	ClassificationKNN
  Linear classification model	ClassificationLinear
  Multiclass naive Bayes model	ClassificationNaiveBayes, CompactClassificationNaiveBayes
  Neural network classifier	ClassificationNeuralNetwork, CompactClassificationNeuralNetwork
  Support vector machine classifier for one-class and binary classification	ClassificationSVM, CompactClassificationSVM
  Binary decision tree for multiclass classification	ClassificationTree, CompactClassificationTree

• Function handle — You can specify a function handle that accepts predictor data and returns a column vector containing a prediction for each observation in the predictor data. The prediction is a predicted response for regression or a predicted score of a single class for classification. You must provide the predictor data using X.

X — Predictor data

numeric matrix | table

Predictor data, specified as a numeric matrix or table. Each row of X corresponds to one observation, and each column corresponds to one variable.

• For a numeric matrix:

  • The variables that makes up the columns of X must have the same order as the predictor variables that trained blackbox, stored in blackbox.X.

  • If you trained blackbox using a table, then X can be a numeric matrix if the table contains all numeric predictor variables.

• For a table:

  • If you trained blackbox using a table (for example, Tbl), then all predictor variables in X must have the same variable names and data types as those in Tbl. However, the column order of X does not need to correspond to the column order of Tbl.

  • If you trained blackbox using a numeric matrix, then the predictor names in blackbox.PredictorNames and the corresponding predictor variable names in X must be the same. To specify predictor names during training, use the PredictorNames name-value argument. All predictor variables in X must be numeric vectors.

  • X can contain additional variables (response variables, observation weights, and so on), but shapley ignores them.

  • shapley does not support multicolumn variables or cell arrays other than cell arrays of character vectors.

If blackbox is a model object that does not contain predictor data or a function handle, you must provide X. If blackbox is a full machine learning model object and you specify this argument, then shapley does not use the predictor data in blackbox; it uses the specified predictor data only.

Data Types: single | double

queryPoints — Query points

numeric matrix | table

Query points at which shapley explains a prediction, specified as a numeric matrix or a table. Each row of queryPoints corresponds to one query point.

• For a numeric matrix:

  • The variables that make up the columns of queryPoints must have the same order as X or the predictor variables that trained blackbox, stored in blackbox.X.

  • If you trained blackbox using a table, then queryPoints can be a numeric matrix if the table contains all numeric variables.

• For a table:

  • If you trained blackbox using a table (for example, Tbl), then all predictor variables in queryPoints must have the same variable names and data types as those in Tbl. However, the column order of queryPoints does not need to correspond to the column order of Tbl.

  • If you trained blackbox using a numeric matrix, then the predictor names in blackbox.PredictorNames and the corresponding predictor variable names in queryPoints must be the same. To specify predictor names during training, use the PredictorNames name-value argument. All predictor variables in queryPoints must be numeric vectors.

  • queryPoints can contain additional variables (response variables, observation weights, and so on), but shapley ignores them.

  • shapley does not support multicolumn variables or cell arrays other than cell arrays of character vectors.

If queryPoints contains NaNs for continuous predictors and Method is "conditional", then the Shapley values (Shapley) in the returned object are NaNs. If you use a regression model that is a Gaussian process regression (GPR), kernel, linear, neural network, or support vector machine (SVM) model, then shapley returns NaN Shapley values for query points that contain missing predictor values or categories not seen during training. For all other models, shapley handles missing values in queryPoints in the same way as blackbox (that is, the predict object function of blackbox or the function handle specified by blackbox).

Before R2024a: You can specify only one query point using QueryPoint=queryPoint, where queryPoint is a row vector of numeric values or a single-row table.

Example: blackbox.X(1,:) specifies the query point as the first observation of the predictor data in the full machine learning model blackbox.

Data Types: single | double | table

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: shapley(blackbox,QueryPoint=q,Method="conditional") creates a shapley object and computes the Shapley values for the query point q using the extension to the Kernel SHAP algorithm.

CategoricalPredictors — Categorical predictors list

vector of positive integers | logical vector | character matrix | string array | cell array of character vectors | "all"

Categorical predictors list, specified as one of the values in this table.

Value	Description
Vector of positive integers	Each entry in the vector is an index value indicating that the corresponding predictor is categorical. The index values are between 1 and p, where p is the number of predictors used to train the model. If blackbox uses a subset of input variables as predictors, then the software indexes the predictors using only the subset. The CategoricalPredictors values do not count the response variable, observation weight variable, or any other variables that the function does not use.
Logical vector	A true entry means that the corresponding predictor is categorical. The length of the vector is p.
Character matrix	Each row of the matrix is the name of a predictor variable. The names must match the variable names of the predictor data in the form of a table. Pad the names with extra blanks so each row of the character matrix has the same length.
String array or cell array of character vectors	Each element in the array is the name of a predictor variable. The names must match the variable names of the predictor data in the form of a table.
"all"	All predictors are categorical.

• If you specify blackbox as a function handle, then shapley identifies categorical predictors from the predictor data X. If the predictor data is in a table, shapley assumes that a variable is categorical if it is a logical vector, unordered categorical vector, character array, string array, or cell array of character vectors. If the predictor data is a matrix, shapley assumes that all predictors are continuous. To identify any other predictors as categorical predictors, specify them by using the CategoricalPredictors name-value argument.

• If you specify blackbox as a regression or classification model object, then shapley identifies categorical predictors by using the CategoricalPredictors property of the model object.

shapley supports an ordered categorical predictor when blackbox supports ordered categorical predictors and you specify Method as "interventional".

Example: CategoricalPredictors="all"

Data Types: single | double | logical | char | string | cell

MaxNumSubsets — Maximum number of predictor subsets

min(2^M,1024) where M is the number of predictors (default) | positive integer

Maximum number of predictor subsets to use for Shapley value computations, specified as a positive integer.

For details on how shapley chooses the subsets to use, see Computational Cost.

This argument is valid only when shapley uses the Kernel SHAP algorithm or the extension to the Kernel SHAP algorithm. If you set the MaxNumSubsets argument when Method is "interventional", the software uses the Kernel SHAP algorithm. For more information, see Algorithms.

Example: MaxNumSubsets=100

Data Types: single | double

Method — Shapley value computation algorithm

"interventional" (default) | "conditional"

Shapley value computation algorithm, specified as "interventional" or "conditional".

• "interventional" (default) — shapley computes the Shapley values with an interventional value function.

  shapley offers three interventional algorithms: Kernel SHAP [1], Linear SHAP [1], and Tree SHAP [2]. The software selects an algorithm based on the machine learning model blackbox and other specified options. For details, see Interventional Algorithms.

• "conditional" — shapley uses the extension to the Kernel SHAP algorithm [3] with a conditional value function.

The Method property stores the name of the selected algorithm. For more information, see Algorithms.

Before R2023a: You can specify this argument as "interventional-kernel" or "conditional-kernel". shapley supports the Kernel SHAP algorithm and the extension of the Kernel SHAP algorithm.

Example: Method="conditional"

Data Types: char | string

NumObservationsToSample — Number of observations to sample from predictor data

100 (default) | "all" | positive integer scalar

Since R2024b

Number of observations to sample from the predictor data, specified as "all" or a positive integer scalar. A value of "all" indicates to use all observations in the predictor data X to compute Shapley values. A value of n indicates to use at most n observations randomly sampled from X. To see the sampled observations, use the SampledObservationIndices property.

Example: NumObservationsToSample="all"

Data Types: single | double | char | string

OutputFcn — Function called after each query point evaluation

[] (default) | function handle

Since R2024a

Function called after each query point evaluation, specified as a function handle. An output function can perform various tasks, such as stopping Shapley value computations, creating variables, or plotting results. For details and examples on how to write your own output functions, see Shapley Output Functions.

This argument is valid only when the shapley function computes Shapley values for multiple query points.

Data Types: function_handle

UseParallel — Flag to run in parallel

false (default) | true

Flag to run in parallel, specified as a numeric or logical 1 (true) or 0 (false). If you specify UseParallel=true, the shapley function executes for-loop iterations by using parfor. The loop runs in parallel when you have Parallel Computing Toolbox™.

This argument is valid only when the shapley function computes Shapley values for multiple query points, or computes Shapley values for one query point by using the Tree SHAP algorithm for an ensemble of trees, the Kernel SHAP algorithm, or the extension to the Kernel SHAP algorithm.

Example: UseParallel=true

Data Types: logical

Properties

BlackboxModel — Machine learning model to be interpreted

Read-only: regression model object | classification model object | function handle

This property is read-only.

Machine learning model to be interpreted, specified as a regression or classification model object or a function handle.

The blackbox argument sets this property.

BlackboxFitted — Predictions for query points computed by machine learning model

Read-only: vector | []

This property is read-only.

Predictions for the query points computed by the machine learning model (BlackboxModel), specified as a vector.

• If BlackboxModel is a model object, then BlackboxFitted contains predicted responses for regression or classified labels for classification.

• If BlackboxModel is a function handle, then BlackboxFitted contains values returned by the function handle, either predicted responses for regression or predicted scores for classification.

The BlackboxFitted property is empty if you do not specify query points.

CategoricalPredictors — Categorical predictor indices

Read-only: vector of positive integers | []

This property is read-only.

Categorical predictor indices, specified as a vector of positive integers. CategoricalPredictors contains index values indicating that the corresponding predictors are categorical. The index values are between 1 and p, where p is the number of predictors used to train the model. If none of the predictors are categorical, then this property is empty ([]).

• If you specify blackbox using a function handle, then shapley identifies categorical predictors from the predictor data X. If you specify the CategoricalPredictors name-value argument, then the argument sets this property.

• If you specify blackbox as a regression or classification model object, then shapley determines this property by using the CategoricalPredictors property of the model object.

shapley supports an ordered categorical predictor when blackbox supports ordered categorical predictors and when you specify Method as "interventional".

Intercept — Average prediction

Read-only: numeric vector | numeric scalar

This property is read-only.

Average prediction, averaged over the predictor data X, specified as a numeric vector or numeric scalar.

• If BlackboxModel is a classification model object, then Intercept is a vector of the average classification scores for each class.

• If BlackboxModel is a regression model object, then Intercept is a scalar of the average response.

• If BlackboxModel is a function handle, then Intercept is a scalar of the average function evaluation.

For a query point, the sum of the Shapley values for all features corresponds to the total deviation of the prediction from the average (Intercept).

MeanAbsoluteShapley — Mean absolute Shapley values

Read-only: table | []

Since R2024a

This property is read-only.

Mean absolute Shapley values, specified as a table. The mean is taken over all query points (QueryPoints).

• For regression, the table has two columns. The first column contains the predictor variable names, and the second column contains the mean absolute Shapley values of the predictors.

• For classification, the table has two or more columns, depending on the number of classes in BlackboxModel. The first column contains the predictor variable names, and the rest of the columns contain the mean absolute Shapley values of the predictors for each class.

The MeanAbsoluteShapley property is empty if you do not specify query points.

Method — Shapley value computation algorithm

Read-only: "interventional-linear" | "interventional-tree" | "interventional-kernel" | "interventional-mix" | "conditional-kernel"

This property is read-only.

Shapley value computation algorithm, specified as "interventional-linear", "interventional-tree", "interventional-kernel", "interventional-mix", or "conditional-kernel".

• "interventional-linear" — shapley uses the Linear SHAP algorithm [1] with an interventional value function. That is, shapley computes interventional Shapley values using the estimated coefficients for linear models.

• "interventional-tree" — shapley uses the Tree SHAP algorithm [2] with an interventional value function.

• "interventional-kernel" — shapley uses the Kernel SHAP algorithm [1] with an interventional value function.

• "interventional-mix" — shapley might not use the same Shapley value computation algorithm for all query points. That is, shapley might use the Tree SHAP algorithm with an interventional value function to compute Shapley values for some query points, and use the Kernel SHAP algorithm with an interventional value function to compute Shapley values for other query points. (since R2024a)

  For an example that shows how to find the method information for specific query points, see Find Method Used for Individual Shapley Value Computations.

• "conditional-kernel" — shapley uses the extension to the Kernel SHAP algorithm [3] with a conditional value function.

The Method argument of shapley or the Method argument of fit sets this property.

For more information, see Algorithms.

NumSubsets — Number of predictor subsets

Read-only: positive integer

This property is read-only.

Number of predictor subsets to use for Shapley value computations, specified as a positive integer.

The MaxNumSubsets argument of shapley or the MaxNumSubsets argument of fit sets this property.

For details on how shapley chooses the subsets to use, see Computational Cost.

QueryPoints — Query points

Read-only: numeric matrix | table | []

This property is read-only.

Query points at which shapley explains predictions using the Shapley values (Shapley), specified as a numeric matrix or a table.

The QueryPoints=queryPoints name-value argument of shapley or the queryPoints argument of fit sets this property.

SampledObservationIndices — Indices of observations sampled from predictor data

Read-only: numeric vector

Since R2024b

This property is read-only.

Indices of the observations sampled from the predictor data X, specified as a numeric vector. To see the sampled observations, use explainer.X(explainer.SampledObservationIndices,:).

The NumObservationsToSample name-value argument of shapley sets this property.

Shapley — Shapley values for query points

Read-only: table | []

This property is read-only.

Shapley values for the query points (QueryPoints), specified as a table.

• For regression, the table has two columns. The first column contains the predictor variable names, and the second column contains the Shapley values of the predictors.

• For classification, the table has two or more columns, depending on the number of classes in BlackboxModel. The first column contains the predictor variable names, and the rest of the columns contain the Shapley values of the predictors for each class.

The Shapley property is empty if you do not specify query points.

For an example that shows how to find Shapley values for one query point after fitting multiple query points, see Investigate One Query Point After Fitting Multiple Query Points.

Before R2024b: The Shapley property is named ShapleyValues.

X — Predictor data

Read-only: numeric matrix | table

This property is read-only.

Predictor data, specified as a numeric matrix or table.

Each row of X corresponds to one observation, and each column corresponds to one variable.

• If you specify the X argument, then it sets this property.

• If you specify blackbox as a full machine learning model object and do not specify X, then this property value is the predictor data used to train blackbox.

If an observation contains NaNs for continuous predictors and Method is "conditional-kernel", then shapley does not use the observation for the Shapley value computation. Similarly, if an observation contains missing predictor values or categories not seen during training, and BlackboxModel is a regression model of type GPR, kernel, linear, neural network, or SVM, then shapley omits the observation from the Shapley value computation. Otherwise, shapley handles missing values in X in the same way as BlackboxModel (that is, the predict object function of BlackboxModel or the function handle specified by BlackboxModel).

shapley stores all observations, including the rows with missing values, in this property.

Object Functions

fit	Compute Shapley values for query points
plot	Plot Shapley values using bar graphs
plotDependence	Plot dependence of Shapley values on predictor values
boxchart	Visualize Shapley values using box charts (box plots)
swarmchart	Visualize Shapley values using swarm scatter charts

Examples

Compute Shapley Values When Creating shapley Object

Train a classification model and create a shapley object. When you create a shapley object, specify a query point so that the software computes the Shapley values for the query point. Then create a bar graph of 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. Specify a query point so that the software computes Shapley values and stores them in the Shapley property.

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,QueryPoints=queryPoint)

explainer = 
shapley explainer with the following local Shapley values:

    Predictor        AAA           AA            A            BBB            BB             B            CCC    
    __________    _________    __________    __________    __________    ___________    __________    __________

    "WC_TA"        0.054853      0.022849     0.0082629     3.418e-07      -0.031172     -0.045745     -0.044031
    "RE_TA"         0.17254      0.093639      0.048798     -0.015662      -0.097291      -0.22498      -0.31434
    "EBIT_TA"     0.0012558     0.0005285    0.00038919    5.0004e-05    -0.00076196    -0.0014544    -0.0012907
    "MVE_BVTD"       1.3942        1.3051       0.53214      -0.27713       -0.88189       -1.1197      -0.87933
    "S_TA"        -0.012379    -0.0080417    0.00013755    -0.0020191    -0.00019923     0.0018047    -0.0026414
    "Industry"      -0.1102     -0.057898    -0.0019888       0.08099       0.097352       0.11483       0.16764


  Properties, Methods

By default, shapley subsamples 100 observations from the data in blackbox.X to compute the Shapley values. For faster computation, use a smaller sample of the training set or specify UseParallel as true.

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"        0.054853      0.022849     0.0082629     3.418e-07      -0.031172     -0.045745     -0.044031
    "RE_TA"         0.17254      0.093639      0.048798     -0.015662      -0.097291      -0.22498      -0.31434
    "EBIT_TA"     0.0012558     0.0005285    0.00038919    5.0004e-05    -0.00076196    -0.0014544    -0.0012907
    "MVE_BVTD"       1.3942        1.3051       0.53214      -0.27713       -0.88189       -1.1197      -0.87933
    "S_TA"        -0.012379    -0.0080417    0.00013755    -0.0020191    -0.00019923     0.0018047    -0.0026414
    "Industry"      -0.1102     -0.057898    -0.0019888       0.08099       0.097352       0.11483       0.16764

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.

Create shapley Object and Compute Shapley Values Using fit

Train a regression model and create a shapley object. When you create a shapley object, if you do not specify query points, then the software does not compute Shapley values. Use the object function fit to compute the Shapley values for a specified query point. Then create a bar graph of the Shapley values by using the object function plot.

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. Specify the data set tbl, because mdl does not contain training data.

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. The fit object function uses the sampled observations instead of 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.

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 prediction for the query point from the average, due to the corresponding variable.

Investigate One Query Point After Fitting Multiple Query Points

Train a classification model and create a shapley object. Visualize the Shapley values for multiple query points by using the swarmchart object function. Find the Shapley values for any query points of interest.

Load the fisheriris data set, which contains measurements for 150 irises, and create a table. SepalLength, SepalWidth, PetalLength, and PetalWidth are the predictor variables, and Species is the response variable.

fisheriris = readtable("fisheriris.csv");

Partition the data into training and test sets. Use 75% of the observations to create the training set and 25% of the observations to create the test set.

rng("default")
c = cvpartition(fisheriris.Species,"Holdout",0.25);
trainTbl = fisheriris(training(c),:);
testTbl = fisheriris(test(c),:);

Train a classification model by using the fitcnet function. Standardize the predictor variables, and specify the order of the classes.

mdl = fitcnet(trainTbl,"Species",Standardize=true, ...
    ClassNames={'setosa','versicolor','virginica'});

Create a shapley object that explains the predictions for multiple query points. Use the test set data to compute the Shapley values, and specify the observations in the test set as the query points.

explainer = shapley(mdl,testTbl,QueryPoints=testTbl)

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

      Predictor       setosa     versicolor    virginica
    _____________    ________    __________    _________

    "SepalLength"     0.12466     0.12539      0.066055 
    "SepalWidth"     0.027488     0.03004      0.016665 
    "PetalLength"     0.17226     0.14164       0.18777 
    "PetalWidth"      0.11795     0.17135       0.23687 


  Properties, Methods

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

For each predictor and class, the mean absolute Shapley value is the absolute value of the Shapley values, averaged across all query points.

Visualize the distribution of the Shapley values for the default class (setosa) by using the swarmchart object function.

swarmchart(explainer)

For each predictor, the function displays the Shapley values for the query points. The corresponding swarm chart shows the distribution of the Shapley values. The function determines the order of the predictors by using the mean absolute Shapley values.

Find the observation with the lowest SepalWidth Shapley value for class setosa. Use data tips to find the index of the observation in the set of query points.

The query point is the 17th observation in the set of query points.

Find the observation's Shapley values in the Shapley property of explainer.

First, define a custom function named localShapley that returns a table of Shapley values for the observation with the specified query point index (queryPointIndex) in the specified shapley object (explainer).

function queryPointTbl= localShapley(explainer,queryPointIndex)
    tbl = explainer.Shapley(:,2:end);
    queryPointTbl = varfun(@(x)x(:,queryPointIndex),tbl);
    queryPointTbl.Properties.VariableNames = tbl.Properties.VariableNames;
    queryPointTbl = [explainer.Shapley(:,1) queryPointTbl];
end

Return the Shapley values for the query point with index 17.

results = localShapley(explainer,17)

results=4×4 table
      Predictor       setosa     versicolor    virginica
    _____________    ________    __________    _________

    "SepalLength"     0.06193    -0.028438     -0.033492
    "SepalWidth"      -0.1135     0.088441       0.02506
    "PetalLength"     -0.1543      0.31506      -0.16076
    "PetalWidth"     -0.11846      0.35579      -0.23734

Plot the query point Shapley values using the plot object function.

plot(explainer,QueryPointIndices=17)

By default, the function plots the Shapley values for the versicolor class because it is the predicted class for the query point.

Specify Blackbox Model Using Function Handle

Train a regression model and create a shapley object using a function handle to the predict function of the model. Use the object function fit to compute the Shapley values for the specified query point. Then plot the Shapley values by using the object function plot.

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.

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

Train a blackbox model of MPG by using the TreeBagger function.

rng("default") % For reproducibility
Mdl = TreeBagger(100,tbl,MPG,Method="regression", ...
    CategoricalPredictors=[2 5]);

shapley does not support a TreeBagger object directly, so you cannot specify the first input argument (blackbox model) of shapley as a TreeBagger object. Instead, you can use a function handle to the predict function. You can also specify options of the predict function using name-value arguments of the function.

Create the function handle to the predict function of the TreeBagger object Mdl. Specify the array of tree indices to use as 1:50.

f = @(tbl) predict(Mdl,tbl,Trees=1:50);

Create a shapley object using the function handle f. When you specify a blackbox model as a function handle, you must provide the predictor data. tbl includes categorical predictors (Cylinder and Model_Year) with the double data type. By default, shapley does not treat variables with the double data type as categorical predictors. Specify the second (Cylinder) and fifth (Model_Year) variables as categorical predictors.

explainer = shapley(f,tbl,CategoricalPredictors=[2 5]);
explainer = fit(explainer,tbl(1,:));

Plot the Shapley values.

plot(explainer)

More About

Shapley Values

In game theory, the Shapley value of a player is the average marginal contribution of the player in a cooperative game. In the context of machine learning prediction, the Shapley value of a feature for a query point explains the contribution of the feature to a prediction (response for regression or score of each class for classification) at the specified query point.

The Shapley value of a feature for a query point is the contribution of the feature to the deviation from the average prediction. For a query point, the sum of the Shapley values for all features corresponds to the total deviation of the prediction from the average. That is, the sum of the average prediction and the Shapley values for all features corresponds to the prediction for the query point.

For more details, see Shapley Values for Machine Learning Model.

References

[1] Lundberg, Scott M., and S. Lee. "A Unified Approach to Interpreting Model Predictions." Advances in Neural Information Processing Systems 30 (2017): 4765–774.

[2] Lundberg, Scott M., G. Erion, H. Chen, et al. "From Local Explanations to Global Understanding with Explainable AI for Trees." Nature Machine Intelligence 2 (January 2020): 56–67.

[3] Aas, Kjersti, Martin Jullum, and Anders Løland. "Explaining Individual Predictions When Features Are Dependent: More Accurate Approximations to Shapley Values." Artificial Intelligence 298 (September 2021).

Extended Capabilities

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set the UseParallel name-value argument to true in the call to this function.

For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

Version History

Introduced in R2021a

R2024b: Speed up Shapley value computations

To speed up Shapley value computations, the shapley function now uses a default maximum of 100 observations from the predictor data set X to compute Shapley values. You can set the number of predictor data observations to use by specifying the NumObservationsToSample name-value argument. You can find the indices of the sampled observations in the SampledObservationIndices property of the shapley object.

In previous releases, shapley uses the entire predictor data set to compute Shapley values. To replicate this behavior, set the NumObservationsToSample value to "all".

You can also run Shapley value computations in parallel when using the OutputFcn name-value argument by setting the UseParallel value to true. To parallelize Shapley value computations, you must have Parallel Computing Toolbox.

R2024b: Shapley values property is now named Shapley

The Shapley property of the shapley object contains the Shapley values for the query points. In previous releases, the Shapley property is named ShapleyValues.

When you use a regression model (blackbox) to compute Shapley values, the Shapley property contains a table whose second column is Value. In previous releases, the column name is ShapleyValue.

R2024a: Compute Shapley values for multiple query points

You can now compute Shapley values for multiple query points by using the QueryPoints=queryPoints name-value argument. Before R2024a, you could specify only one query point using QueryPoint=queryPoint, where queryPoint is a row vector of numeric values or a single-row table.

The shapley object contains a new property MeanAbsoluteShapley, which contains the absolute Shapley values, averaged across all query points. Additionally, the Method property can now have the value "interventional-mix". This value indicates that the software might not use the same Shapley value computation algorithm for all query points.

When computing Shapley values for multiple query points, you can use an output function to perform various tasks, such as stopping Shapley value computations, creating variables, or plotting results. To do so, use the OutputFcn name-value argument.

R2023b: Interventional Tree SHAP algorithm supports data with missing predictor values

When observations in the input predictor data (blackbox.X or X) or values in the query point (queryPoint) contain missing values and the Method value is "interventional", the shapley function can use the Tree SHAP algorithm for tree models and ensemble models of tree learners. In previous releases, under these conditions, the shapley function always used the Kernel SHAP algorithm for tree-based models. For more information, including cases where the software still uses Kernel SHAP instead of Tree SHAP for tree-based models, see Interventional Algorithms.

R2023b: Improved performance of Tree SHAP algorithm for tree-based models

The shapley function shows improved performance when computing Shapley values for tree models and ensemble models of tree learners by using the Tree SHAP algorithm with an interventional value function (see Method). The performance increase is sensitive to the values of the shapley input arguments (such as the predictor data, machine learning model, and query point). For example, the Shapley value computation in this code is about 36x faster than in the previous release:

function timingTest

% Generate data set
rng("default")
numObservations = 1e5;
numPredictors = 10;
X = rand(numObservations,numPredictors);
Y = rand(numObservations,1);

% Train model
mdl = fitrensemble(X,Y,Learners="tree");

% Compute Shapley value
tic
shapley(mdl,"QueryPoint",X(50,:),Method="interventional");
toc

end

The approximate execution times are:

• R2023b: 3s

• R2023a: 107s

The code was timed on a Windows^® 10, Intel^® Xeon^® CPU E5-1650 v4 @ 3.60GHz test system by calling the function timingTest.

R2023a: shapley supports the Linear SHAP and Tree SHAP algorithms

shapley supports the Linear SHAP [1] algorithm for linear models and the Tree SHAP [2] algorithm for tree models and ensemble models of tree learners.

If you specify the Method name-value argument as 'interventional' (default), shapley selects an algorithm based on the machine learning model type of blackbox. The Method property stores the name of the selected algorithm.

R2023a: Values of the Method name-value argument have changed

The supported values of the Method name-value argument have changed from 'interventional-kernel' and 'conditional-kernel' to 'interventional' and 'conditional', respectively.