可解释性

训练可解释的回归模型和解释复杂的回归模型

使用本质上可解释的回归模型，如线性模型、决策树和广义加性模型，或使用可解释性特征，来解释本质上不可解释的复杂回归模型。

要了解如何解释回归模型，请参阅 Interpret Machine Learning Models。

函数

`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`	Shapley values
`fit`	Compute Shapley values for query point
`plot`	Plot Shapley values

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

`fitlm`	拟合线性回归模型
`fitrgam`	Fit generalized additive model (GAM) for regression
`fitrlinear`	Fit linear regression model to high-dimensional data
`fitrtree`	Fit binary decision tree for regression

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

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 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.

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.