Main Content

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

Shapley Values

shapleyShapley values
fitCompute Shapley values for query point
plotPlot Shapley values

Partial Dependence

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

Objects

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

Topics

Model Interpretation

Interpret Machine Learning Models

Explain model predictions using lime, shapley, and plotPartialDependence.

Shapley Values for Machine Learning Model

Compute Shapley values for a machine learning model using two algorithms: kernelSHAP and the extension to kernelSHAP.

Introduction to Feature Selection

Learn about feature selection algorithms and explore the functions available for feature selection.

Interpretable Models

Train Generalized Additive Model for Binary Classification

Train a generalized additive model (GAM) with optimal parameters, assess predictive performance, and interpret the trained model.

Train Decision Trees Using Classification Learner App

Create and compare classification trees, and export trained models to make predictions for new data.

Classification Using Nearest Neighbors

Categorize data points based on their distance to points in a training data set, using a variety of distance metrics.