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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Interpret Trained Model

Local Interpretable Model-Agnostic Explanations (LIME)

`lime`	Local interpretable model-agnostic explanations (LIME) (Since R2020b)
`fit`	Fit simple model of local interpretable model-agnostic explanations (LIME) (Since R2020b)
`plot`	Plot results of local interpretable model-agnostic explanations (LIME) (Since R2020b)

Shapley Values

`shapley`	Shapley values (Since R2021a)
`fit`	Compute Shapley values for query points (Since R2021a)
`plot`	Plot Shapley values using bar graphs (Since R2021a)
`boxchart`	Visualize Shapley values using box charts (box plots) (Since R2024a)
`swarmchart`	Visualize Shapley values using swarm scatter charts (Since R2024a)

Partial Dependence

`partialDependence`	Compute partial dependence (Since R2020b)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots

Train Interpretable Model

`fitlm`	Fit linear regression model
`fitrgam`	Fit generalized additive model (GAM) for regression (Since R2021a)
`fitrlinear`	Fit linear regression model to high-dimensional data
`fitrtree`	Fit binary decision tree for regression

Objects

`LinearModel`	Linear regression model
`RegressionGAM`	Generalized additive model (GAM) for regression (Since R2021a)
`RegressionLinear`	Linear regression model for high-dimensional data
`RegressionTree`	Regression tree

Topics

Model Interpretation

Interpret Machine Learning Models
Explain model predictions using the lime and shapley objects and the plotPartialDependence function.
Shapley Values for Machine Learning Model
Compute Shapley values for a machine learning model using interventional algorithm or conditional algorithm.
Shapley Output Functions
Stop Shapley computations, create plots, save information to your workspace, or perform calculations while using shapley.
Introduction to Feature Selection
Learn about feature selection algorithms and explore the functions available for feature selection.
Explain Model Predictions for Regression Models Trained in Regression Learner App
To understand how trained regression models use predictors to make predictions, use global and local interpretability tools, such as partial dependence plots, LIME values, and Shapley values.
Use Partial Dependence Plots to Interpret Regression Models Trained in Regression Learner App
Determine how features are used in trained regression models by creating partial dependence plots.

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.