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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

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
fitcgamFit generalized additive model (GAM) for binary classification
fitclinearFit binary linear classifier to high-dimensional data
fitctreeFit binary decision tree for multiclass classification

对象

ClassificationGAMGeneralized additive model (GAM) for binary classification
ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationTreeBinary decision tree for multiclass classification

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 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.