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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

全部展开

与模型无关的局部可解释性解释 (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 值

shapleyShapley values
fitCompute Shapley values for query point
plotPlot Shapley values

部分依赖

partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
fitlm拟合线性回归模型
fitrgamFit generalized additive model (GAM) for regression
fitrlinearFit linear regression model to high-dimensional data
fitrtreeFit binary decision tree for regression

对象

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

主题

模型解释

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.

可解释模型

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.