partialDependence
Compute partial dependence
Syntax
Description
computes the partial dependence pd
= partialDependence(RegressionMdl
,Vars
)pd
between the predictor variables
listed in Vars
and the responses predicted by using the regression
model RegressionMdl
, which contains predictor data.
computes the partial dependence pd
= partialDependence(ClassificationMdl
,Vars
,Labels
)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.
uses additional options specified by one or more name-value arguments. For example, if you
specify pd
= partialDependence(___,Name,Value
)"UseParallel","true"
, the
partialDependence
function uses parallel computing to perform the
partial dependence calculations.
Examples
Input Arguments
Output Arguments
More About
Algorithms
For both a regression model (RegressionMdl
) and a classification
model (ClassificationMdl
), partialDependence
uses a
predict
function to predict responses or scores.
partialDependence
chooses the proper predict
function according to the model 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
Alternative Functionality
plotPartialDependence
computes and plots partial dependence values. The function can also create individual conditional expectation (ICE) plots.
References
[2] Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. New York, NY: Springer New York, 2009.