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)
`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

`fitlm`	Fit linear regression model
`fitrgam`	Fit generalized additive model (GAM) for regression
`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
`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.
Introduction to Feature Selection
Learn about feature selection algorithms and explore the functions available for feature selection.
Interpret Regression Models Trained in Regression Learner App
Determine how features are used in trained regression models by using 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.