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Interpretability

Train interpretable classification models and interpret complex classification models

Use inherently interpretable classification models, such as linear models, decision trees, and generalized additive models, or use interpretability features to interpret complex classification models that are not inherently interpretable.

To learn how to interpret classification models, see Interpret Machine Learning Models.

Functions

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Local Interpretable Model-Agnostic Explanations (LIME)

limeLocal interpretable model-agnostic explanations (LIME) (Since R2020b)
fitFit simple model of local interpretable model-agnostic explanations (LIME) (Since R2020b)
plotPlot results of local interpretable model-agnostic explanations (LIME) (Since R2020b)

Shapley Values

shapleyShapley values (Since R2021a)
fitCompute Shapley values for query point (Since R2021a)
plotPlot Shapley values (Since R2021a)

Partial Dependence

partialDependenceCompute partial dependence (Since R2020b)
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
fitcgamFit generalized additive model (GAM) for binary classification (Since R2021a)
fitclinearFit binary linear classifier to high-dimensional data
fitctreeFit binary decision tree for multiclass classification

Objects

ClassificationGAMGeneralized additive model (GAM) for binary classification (Since R2021a)
ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationTreeBinary decision tree for multiclass classification

Topics

Model Interpretation

Interpretable Models