Interpretabilità

Addestrare modelli di regressione interpretabili e interpretare modelli di regressione complessi

Utilizzare modelli di regressione intrinsecamente interpretabili, quali modelli lineari, alberi decisionali e modelli additivi generalizzati oppure avvalersi di feature di interpretabilità per interpretare modelli di regressione complessi che non sono intrinsecamente interpretabili.

Per scoprire come interpretare i modelli di regressione, vedere Interpret Machine Learning Models.

Funzioni

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Interpretazione del modello addestrato

Spiegazioni locali interpretabili e indipendenti dal modello (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)

Valori di Shapley

`shapley`	Shapley values
`fit`	Compute Shapley values for query points
`plot`	Plot Shapley values using bar graphs
`boxchart`	Visualize Shapley values using box charts (box plots) (Da R2024a)
`plotDependence`	Plot dependence of Shapley values on predictor values (Da R2024b)
`swarmchart`	Visualize Shapley values using swarm scatter charts (Da R2024a)

Dipendenza parziale

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

Addestramento di un modello interpretabile

`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

Oggetti

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

Argomenti

Interpretazione del modello

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 permutation importance plots, 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.

Modelli interpretabili

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.