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, seeInterpret Machine Learning Models.

Functions

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Interpret Trained Model

Local Interpretable Model-Agnostic Explanations (LIME)

`lime`	Local interpretable model-agnostic explanations (LIME)
`fit`	Fit simple model of local interpretable model-agnostic explanations (LIME)
`plot`	Plot results of local interpretable model-agnostic explanations (LIME)

Shapley Values

`shapley`	Shapley values
`fit`	Compute Shapley values for query point
`plot`	Plot Shapley values

Partial Dependence

`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots

Train Interpretable Model

`fitcgam`	Fit generalized additive model (GAM) for binary classification
`fitclinear`	Fit binary linear classifier to high-dimensional data
`fitctree`	Fit binary decision tree for multiclass classification

Objects

`ClassificationGAM`	Generalized additive model (GAM) for binary classification
`ClassificationLinear`	Linear model for binary classification of high-dimensional data
`ClassificationTree`	Binary decision tree for multiclass classification