可解释性 -凯发k8网页登录

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

`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

	fit generalized additive model (gam) for binary classification
`fitclinear`	fit binary linear classifier to high-dimensional data
`fitctree`	fit binary decision tree for multiclass classification

	generalized additive model (gam) for binary classification
`classificationlinear`	linear model for binary classification of high-dimensional data
	binary decision tree for multiclass classification

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 interventional algorithm or conditional algorithm.
introduction to feature selection
learn about feature selection algorithms and explore the functions available for feature selection.
determine how features are used in trained classifiers by using partial dependence plots.

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.
categorize data points based on their distance to points in a training data set, using a variety of distance metrics.