partialDependence

Compute partial dependence

Syntax

pd = partialDependence(RegressionMdl,Vars)

pd = partialDependence(ClassificationMdl,Vars,Labels)

pd = partialDependence(___,Data)

pd = partialDependence(___,Name,Value)

[pd,x,y] = partialDependence(___)

Description

pd = partialDependence(RegressionMdl,Vars) computes the partial dependence pd between the predictor variables listed in Vars and the responses predicted by using the regression model RegressionMdl, which contains predictor data.

pd = partialDependence(ClassificationMdl,Vars,Labels) computes the partial dependence pd between the predictor variables listed in Vars and the scores for the classes specified in Labels by using the classification model ClassificationMdl, which contains predictor data.

pd = partialDependence(___,Data) uses new predictor data in Data. You can specify Data in addition to any of the input argument combinations in the previous syntaxes.

pd = partialDependence(___,Name,Value) uses additional options specified by one or more name-value pair arguments. For example, if you specify 'UseParallel','true', the partialDependence function uses parallel computing to perform the partial dependence calculations.

[pd,x,y] = partialDependence(___) also returns x and y, which contain the query points of the first and second predictor variables in Vars, respectively. If you specify one variable in Vars, then partialDependence returns an empty matrix ([]) for y.

Examples

Compute and Plot Partial Dependence on One Variable

Train a naive Bayes classification model with the fisheriris data set, and compute partial dependence values that show the relationship between the predictor variable and the predicted scores (posterior probabilities) for multiple classes.

Load the fisheriris data set, which contains species (species) and measurements (meas) on sepal length, sepal width, petal length, and petal width for 150 iris specimens. The data set contains 50 specimens from each of three species: setosa, versicolor, and virginica.

load fisheriris

Train a naive Bayes classification model with species as the response and meas as predictors.

Mdl = fitcnb(meas,species,'PredictorNames',["Sepal Length","Sepal Width","Petal Length","Petal Width"]);

Compute partial dependence values on the third predictor variable (petal length) of the scores predicted by Mdl for all three classes of species. Specify the class labels by using the ClassNames property of Mdl.

[pd,x] = partialDependence(Mdl,3,Mdl.ClassNames);

pd contains the partial dependence values for the query points x. You can plot the computed partial dependence values by using plotting functions such as plot and bar. Plot pd against x by using the bar function.

bar(x,pd)
legend(Mdl.ClassNames)
xlabel("Petal Length")
ylabel("Scores")
title("Partial Dependence Plot")

According to this model, the probability of virginica increases with petal length. The probability of setosa is about 0.33, from where petal length is 0 to around 2.5, and then the probability drops to almost 0.

Alternatively, you can use the plotPartialDependence function to compute and plot partial dependence values.

plotPartialDependence(Mdl,3,Mdl.ClassNames)

Compute and Plot Partial Dependence on Two Variables for Multiple Classes

Train an ensemble of classification models and compute partial dependence values on two variables for multiple classes. Then plot the partial dependence values for each class.

Load the census1994 data set, which contains US yearly salary data, categorized as <=50K or >50K, and several demographic variables.

load census1994

Extract a subset of variables to analyze from the table adultdata.

X = adultdata(1:500,{'age','workClass','education_num','marital_status','race', ...
   'sex','capital_gain','capital_loss','hours_per_week','salary'});

Train a random forest of classification trees by using fitcensemble and specifying 'Method' as 'Bag'. For reproducibility, use a template of trees created by using templateTree with the 'Reproducible' option.

rng('default')
t = templateTree('Reproducible',true);
Mdl = fitcensemble(X,'salary','Method','Bag','Learners',t);

Inspect the class names in Mdl.

Mdl.ClassNames

ans = 2x1 categorical
     <=50K 
     >50K 

Compute partial dependence values of the scores on the predictors age and education_num for both classes (<=50K and >50K). Specify the number of observations to sample as 100.

[pd,x,y] = partialDependence(Mdl,{'age','education_num'},Mdl.ClassNames,'NumObservationsToSample',100);

Create a surface plot of the partial dependence values for the first class (<=50K) by using the surfl function.

figure
surf(x,y,squeeze(pd(1,:,:)))
xlabel('age')
ylabel('education\_num')
zlabel('Score of class <=50K')
title('Partial Dependence Plot')
view([130 30]) % Modify the viewing angle

Create a surface plot of the partial dependence values for the second class (>50K).

figure
surf(x,y,squeeze(pd(2,:,:)))
xlabel('age')
ylabel('education\_num')
zlabel('Score of class >50K')
title('Partial Dependence Plot')
view([130 30]) % Modify the viewing angle

The two plots show different partial dependence patterns depending on the class.

Compute and Plot Partial Dependence on Multiple Variables for Regression

Train a support vector machine (SVM) regression model using the carsmall data set, and compute the partial dependence on two predictor variables. Then, create a figure that shows the partial dependence on the two variables along with the histogram on each variable.

Load the carsmall data set.

load carsmall

Create a table that contains Weight, Cylinders, Displacement, and Horsepower.

Tbl = table(Weight,Cylinders,Displacement,Horsepower);

Train an SVM regression model using the predictor variables in Tbl and the response variable MPG. Use a Gaussian kernel function with an automatic kernel scale.

Mdl = fitrsvm(Tbl,MPG,'ResponseName','MPG', ...
    'CategoricalPredictors','Cylinders','Standardize',true, ...
    'KernelFunction','gaussian','KernelScale','auto');

Compute the partial dependence of the predicted response (MPG) on the predictor variables Weight and Horsepower. Specify query points to compute the partial dependence by using the 'QueryPoints' name-value pair argument.

numPoints = 10;
ptX = linspace(min(Weight),max(Weight),numPoints)';
ptY = linspace(min(Horsepower),max(Horsepower),numPoints)';
[pd,x,y] = partialDependence(Mdl,{'Weight','Horsepower'},'QueryPoints',[ptX ptY]);

Create a figure that contains a 5-by-5 tiled chart layout. Plot the partial dependence on the two variables by using the imagesc function. Then draw the histogram for each variable by using the histogram function. Specify the edges of the histograms so that the centers of the histogram bars align with the query points. Change the axes properties to align the axes of the plots.

t = tiledlayout(5,5,'TileSpacing','compact');

ax1 = nexttile(2,[4,4]);
imagesc(x,y,pd)
title('Partial Dependence Plot')
colorbar('eastoutside')
ax1.YDir = 'normal';

ax2 = nexttile(22,[1,4]);
dX = diff(ptX(1:2));
edgeX = [ptX-dX/2;ptX(end)+dX];
histogram(Weight,edgeX);
xlabel('Weight')
xlim(ax1.XLim);

ax3 = nexttile(1,[4,1]);
dY = diff(ptY(1:2));
edgeY = [ptY-dY/2;ptY(end)+dY];
histogram(Horsepower,edgeY)
xlabel('Horsepower')
xlim(ax1.YLim);
ax3.XDir = 'reverse';
camroll(-90)

Each element of pd specifies the color for one pixel of the image plot. The histograms aligned with the axes of the image show the distribution of the predictors.

Input Arguments

RegressionMdl — Regression model

regression model object

Regression model, specified as a full or compact regression model object, as given in the following tables of supported models.

Model	Full or Compact Model Object
Generalized linear model	GeneralizedLinearModel, CompactGeneralizedLinearModel
Generalized linear mixed-effect model	GeneralizedLinearMixedModel
Linear regression	LinearModel, CompactLinearModel
Linear mixed-effect model	LinearMixedModel
Nonlinear regression	NonLinearModel
Ensemble of regression models	RegressionEnsemble, RegressionBaggedEnsemble, CompactRegressionEnsemble
Generalized additive model (GAM)	RegressionGAM, CompactRegressionGAM
Gaussian process regression	RegressionGP, CompactRegressionGP
Gaussian kernel regression model using random feature expansion	RegressionKernel
Linear regression for high-dimensional data	RegressionLinear
Neural network regression model	RegressionNeuralNetwork, CompactRegressionNeuralNetwork
Support vector machine (SVM) regression	RegressionSVM, CompactRegressionSVM
Regression tree	RegressionTree, CompactRegressionTree
Bootstrap aggregation for ensemble of decision trees	TreeBagger, CompactTreeBagger

If RegressionMdl is a model object that does not contain predictor data (for example, a compact model), you must provide the input argument Data.

partialDependence does not support a model object trained with a sparse matrix. When you train a model, use a full numeric matrix or table for predictor data where rows correspond to individual observations.

ClassificationMdl — Classification model

classification model object

Classification model, specified as a full or compact classification model object, as given in the following tables of supported models.

Model	Full or Compact 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
Generalized additive model (GAM)	ClassificationGAM, CompactClassificationGAM
Gaussian kernel classification model using random feature expansion	ClassificationKernel
k-nearest neighbor classifier	ClassificationKNN
Linear classification model	ClassificationLinear
Multiclass naive Bayes model	ClassificationNaiveBayes, CompactClassificationNaiveBayes
Neural network classifier	ClassificationNeuralNetwork, CompactClassificationNeuralNetwork
Support vector machine (SVM) classifier for one-class and binary classification	ClassificationSVM, CompactClassificationSVM
Binary decision tree for multiclass classification	ClassificationTree, CompactClassificationTree
Bagged ensemble of decision trees	TreeBagger, CompactTreeBagger

If ClassificationMdl is a model object that does not contain predictor data (for example, a compact model), you must provide the input argument Data.

partialDependence does not support a model object trained with a sparse matrix. When you train a model, use a full numeric matrix or table for predictor data where rows correspond to individual observations.

Vars — Predictor variables

vector of positive integers | character vector | string scalar | string array | cell array of character vectors

Predictor variables, specified as a vector of positive integers, character vector, string scalar, string array, or cell array of character vectors. You can specify one or two predictor variables, as shown in the following tables.

One Predictor Variable

Value	Description
positive integer	Index value corresponding to the column of the predictor data.
character vector or string scalar	Name of a predictor variable. The name must match the entry in RegressionMdl.PredictorNames or ClassificationMdl.PredictorNames.

Two Predictor Variables

Value	Description
vector of two positive integers	Index values corresponding to the columns of the predictor data.
string array or cell array of character vectors	Names of predictor variables. Each element in the array is the name of a predictor variable. The names must match the entries in RegressionMdl.PredictorNames or ClassificationMdl.PredictorNames.

Example: {'x1','x3'}

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

Labels — Class labels

categorical array | character array | logical vector | numeric vector | cell array of character vectors

Class labels, specified as a categorical or character array, logical or numeric vector, or cell array of character vectors. The values and data types in Labels must match those of the class names in the ClassNames property of ClassificationMdl (ClassificationMdl.ClassNames).

You can specify one or multiple class labels.

This argument is valid only when ClassificationMdl is a classification model object.

Example: {'red','blue'}

Example: ClassificationMdl.ClassNames([1 3]) specifies Labels as the first and third classes in ClassificationMdl.

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

Data — Predictor data

numeric matrix | table

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

Data must be consistent with the predictor data that trained the model (RegressionMdl or ClassificationMdl), stored in either the X or Variables property.

• If you trained the model using a numeric matrix, then Data must be a numeric matrix. The variables making up the columns of Data must have the same number and order as the predictor variables that trained the model.

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

• partialDependence does not support a sparse matrix.

If RegressionMdl or ClassificationMdl is a model object that does not contain predictor data, you must provide Data. If the model is a full model object that contains predictor data and you specify this argument, then partialDependence does not use the predictor data in the model and uses Data only.

Data Types: single | double | table

Name-Value Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: partialDependence(Mdl,Vars,Data,'NumObservationsToSample',100,'UseParallel',true) computes the partial dependence values by using 100 sampled observations in Data and executing for-loop iterations in parallel.

IncludeInteractions — Flag to include interaction terms

true | false

Flag to include interaction terms of the generalized additive model (GAM) in the partial dependence computation, specified as true or false. This argument is valid only for a GAM. That is, you can specify this argument only when RegressionMdl is RegressionGAM or CompactRegressionGAM, or ClassificationMdl is ClassificationGAM or CompactClassificationGAM.

The default 'IncludeInteractions' value is true if the model contains interaction terms. The value must be false if the model does not contain interaction terms.

Example: 'IncludeInteractions',false

Data Types: logical

IncludeIntercept — Flag to include intercept term

true (default) | false

Flag to include an intercept term of the generalized additive model (GAM) in the partial dependence computation, specified as true or false. This argument is valid only for a GAM. That is, you can specify this argument only when RegressionMdl is RegressionGAM or CompactRegressionGAM, or ClassificationMdl is ClassificationGAM or CompactClassificationGAM.

Example: 'IncludeIntercept',false

Data Types: logical

NumObservationsToSample — Number of observations to sample

number of total observations (default) | positive integer

Number of observations to sample, specified as a positive integer. The default value is the number of total observations in Data or the model (RegressionMdl or ClassificationMdl). If you specify a value larger than the number of total observations, then partialDependence uses all observations.

partialDependence samples observations without replacement by using the datasample function and uses the sampled observations to compute partial dependence.

Example: 'NumObservationsToSample',100

Data Types: single | double

QueryPoints — Points to compute partial dependence

numeric column vector | numeric two-column matrix | cell array of two numeric column vectors

Points to compute partial dependence for numeric predictors, specified as a numeric column vector, a numeric two-column matrix, or a cell array of two numeric column vectors.

• If you select one predictor variable in Vars, use a numeric column vector.

• If you select two predictor variables in Vars:

  • Use a numeric two-column matrix to specify the same number of points for each predictor variable.

  • Use a cell array of two numeric column vectors to specify a different number of points for each predictor variable.

The default value is a numeric column vector or a numeric two-column matrix, depending on the number of selected predictor variables. Each column contains 100 evenly spaced points between the minimum and maximum values of the sampled observations for the corresponding predictor variable.

You cannot modify 'QueryPoints' for a categorical variable. The partialDependence function uses all categorical values in the selected variable.

If you select one numeric variable and one categorical variable, you can specify 'QueryPoints' for a numeric variable by using a cell array consisting of a numeric column vector and an empty array.

Example: 'QueryPoints',{pt,[]}

Data Types: single | double | cell

UseParallel — Flag to run in parallel

false (default) | true

Flag to run in parallel, specified as true or false. If you specify 'UseParallel',true, the partialDependence function executes for-loop iterations in parallel by using parfor when predicting responses or scores for each observation and averaging them. This option requires Parallel Computing Toolbox™.

Example: 'UseParallel',true

Data Types: logical

Output Arguments

pd — Partial dependence values

numeric array

Partial dependence values, returned as a numX-by-numY numeric matrix (for a regression model) or numLabels-by-numX-by-numY numeric array (for a classification model). numX and numY are the number of query points of the first and second variables in Vars, respectively. numLabels is the number of class labels in Labels.

The value in pd(i,j,k) is the partial dependence value of the query point x(j) and y(k) for the ith class label. x(j) is the jth query point of the first predictor variable, and y(k) is the kth query point of the second predictor variable.

x — Query points of first predictor variable

numeric column vector | categorical column vector

Query points of the first predictor variable in Vars, returned as a numeric or categorical column vector.

If the predictor variable is numeric, then you can specify the query points by using the 'QueryPoints' name-value pair argument.

Data Types: single | double | categorical

y — Query points of second predictor variable

numeric column vector | categorical column vector

Query points of the second predictor variable in Vars, returned as a numeric or categorical column vector.

If the predictor variable is numeric, then you can specify the query points by using the 'QueryPoints' name-value pair argument.

Data Types: single | double | categorical

More About

Partial Dependence for Regression Models

Partial dependence[1] represents the relationships between predictor variables and predicted responses in a trained regression model. partialDependence computes the partial dependence of predicted responses on a subset of predictor variables by marginalizing over the other variables.

Consider partial dependence on a subset X^S of the whole predictor variable set X = {x₁, x₂, …, x_m}. A subset X^S includes either one variable or two variables: X^S = {x_S1} or X^S = {x_S1, x_S2}. Let X^C be the complementary set of X^S in X. A predicted response f(X) depends on all variables in X:

f(X) = f(X^S, X^C).

The partial dependence of predicted responses on X^S is defined by the expectation of predicted responses with respect to X^C:

$$f^S\left(X^S\right)=E_C\left[f\left(X^S,X^C\right)\right]={\displaystyle \int f\left(X^S,X^C\right)}{p}_C\left(X^C\right)d{X}^C,$$

where p_C(X^C) is the marginal probability of X^C, that is, $$p_C\left(X^C\right)\approx {\displaystyle \int p}\left(X^S,X^C\right)d{X}^S$$. Assuming that each observation is equally likely, and the dependence between X^S and X^C and the interactions of X^S and X^C in responses is not strong, partialDependence estimates the partial dependence by using observed predictor data as follows:

$$f^S\left(X^S\right)\approx \frac{1}{N}{\displaystyle \sum _{i=1}^{N}f\left(X^S,X_i{}^C\right)},$$

(1)

where N is the number of observations and X_i = (X_i^S, X_i^C) is the ith observation.

When you call the partialDependence function, you can specify a trained model (f(·)) and select variables (X^S) by using the input arguments RegressionMdl and Vars, respectively. partialDependence computes the partial dependence at 100 evenly spaced points of X^S or the points that you specify by using the 'QueryPoints' name-value pair argument. You can specify the number (N) of observations to sample from given predictor data by using the 'NumObservationsToSample' name-value pair argument.

Partial Dependence Classification Models

In the case of classification models, partialDependence computes the partial dependence in the same way as for regression models, with one exception: instead of using the predicted responses from the model, the function uses the predicted scores for the classes specified in Labels.

Weighted Traversal Algorithm

The weighted traversal algorithm[1] is a method to estimate partial dependence for a tree-based model. The estimated partial dependence is the weighted average of response or score values corresponding to the leaf nodes visited during the tree traversal.

Let X^S be a subset of the whole variable set X and X^C be the complementary set of X^S in X. For each X^S value to compute partial dependence, the algorithm traverses a tree from the root (beginning) node down to leaf (terminal) nodes and finds the weights of leaf nodes. The traversal starts by assigning a weight value of one at the root node. If a node splits by X^S, the algorithm traverses to the appropriate child node depending on the X^S value. The weight of the child node becomes the same value as its parent node. If a node splits by X^C, the algorithm traverses to both child nodes. The weight of each child node becomes a value of its parent node multiplied by the fraction of observations corresponding to each child node. After completing the tree traversal, the algorithm computes the weighted average by using the assigned weights.

For an ensemble of bagged trees, the estimated partial dependence is an average of the weighted averages over the individual trees.

Algorithms

partialDependence uses a predict function to predict responses or scores. partialDependence chooses the proper predict function according to the model (RegressionMdl or ClassificationMdl) and runs predict with its default settings. For details about each predict function, see the predict functions in the following two tables. If the specified model is a tree-based model (not including a boosted ensemble of trees), then partialDependence uses the weighted traversal algorithm instead of the predict function. For details, see Weighted Traversal Algorithm.

Regression Model Object

Model Type	Full or Compact Regression Model Object	Function to Predict Responses
Bootstrap aggregation for ensemble of decision trees	CompactTreeBagger	predict
Bootstrap aggregation for ensemble of decision trees	TreeBagger	predict
Ensemble of regression models	RegressionEnsemble, RegressionBaggedEnsemble, CompactRegressionEnsemble	predict
Gaussian kernel regression model using random feature expansion	RegressionKernel	predict
Gaussian process regression	RegressionGP, CompactRegressionGP	predict
Generalized additive model	RegressionGAM, CompactRegressionGAM	predict
Generalized linear mixed-effect model	GeneralizedLinearMixedModel	predict
Generalized linear model	GeneralizedLinearModel, CompactGeneralizedLinearModel	predict
Linear mixed-effect model	LinearMixedModel	predict
Linear regression	LinearModel, CompactLinearModel	predict
Linear regression for high-dimensional data	RegressionLinear	predict
Neural network regression model	RegressionNeuralNetwork, CompactRegressionNeuralNetwork	predict
Nonlinear regression	NonLinearModel	predict
Regression tree	RegressionTree, CompactRegressionTree	predict
Support vector machine	RegressionSVM, CompactRegressionSVM	predict

Classification Model Object

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

Alternative Functionality

• plotPartialDependence computes and plots partial dependence values. The function can also create individual conditional expectation (ICE) plots.

References

[1] Friedman, Jerome. H. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics 29, no. 5 (2001): 1189-1232.

[2] Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. New York, NY: Springer New York, 2009.

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).

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• This function fully supports GPU arrays for the following regression and classification models:

  • LinearModel and CompactLinearModel objects

  • GeneralizedLinearModel and CompactGeneralizedLinearModel objects

  • ClassificationSVM and CompactClassificationSVM objects

• This function supports GPU arrays with limitations for the regression and classification models described in this table.

  Full or Compact Model Object	Limitations
  ClassificationECOC or CompactClassificationECOC	Surrogate splits are not supported for decision tree learners. For KNN learners, you cannot set the following options to the values shown: 'NSMethod','kdtree' 'Distance',function handle 'IncludeTies',true
  ClassificationEnsemble or CompactClassificationEnsemble	Surrogate splits are not supported for decision tree learners. For KNN learners, you cannot set the following options to the values shown: 'NSMethod','kdtree' 'Distance',function handle 'IncludeTies',true
  ClassificationKNN	You cannot set the following options to the values shown: 'NSMethod','kdtree' 'Distance',function handle 'IncludeTies',true
  ClassificationTree or CompactClassificationTree	Surrogate splits are not supported.
  RegressionTree or CompactRegressionTree	Surrogate splits are not supported.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).