可解释性
训练可解释的分类模型和解释复杂的分类模型
使用本质上可解释的分类模型,如线性模型、决策树和广义加性模型,或使用可解释特性来解释本质上不可解释的复波分类模型。
要了解如何解释分类模型,请参阅 Interpret Machine Learning Models。
函数
对象
ClassificationGAM | Generalized additive model (GAM) for binary classification (自 R2021a 起) |
ClassificationLinear | Linear model for binary classification of high-dimensional data |
ClassificationTree | Binary decision tree for multiclass classification |
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 Classifiers Trained in Classification Learner App
Determine how features are used in trained classifiers by creating partial dependence plots.
可解释模型
- Train Generalized Additive Model for Binary Classification
Train a generalized additive model (GAM) with optimal parameters, assess predictive performance, and interpret the trained model. - Train Decision Trees Using Classification Learner App
Create and compare classification trees, and export trained models to make predictions for new data. - Classification Using Nearest Neighbors
Categorize data points based on their distance to points in a training data set, using a variety of distance metrics.