Main Content

Interpretability

Train interpretable regression models and interpret complex regression models

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

To learn how to interpret regression 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
fitlmFit linear regression model
fitrgamFit generalized additive model (GAM) for regression
fitrlinearFit linear regression model to high-dimensional data
fitrtreeFit binary decision tree for regression

Objects

LinearModelLinear regression model
RegressionGAMGeneralized additive model (GAM) for regression
RegressionLinearLinear regression model for high-dimensional data
RegressionTreeRegression tree

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 Linear Regression Model

Train a linear regression model using fitlm to analyze in-memory data and out-of-memory data.

Train Generalized Additive Model for Regression

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

Train Regression Trees Using Regression Learner App

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