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
对象
LinearModel | Linear regression model |
RegressionGAM | Generalized additive model (GAM) for regression (自 R2021a 起) |
RegressionLinear | Linear regression model for high-dimensional data |
RegressionTree | Regression tree |
主题
模型解释
- Interpret Machine Learning Models
Explain model predictions using thelime
andshapley
objects and theplotPartialDependence
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. - 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.
可解释模型
- Train Linear Regression Model
Train a linear regression model usingfitlm
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.