해석 가능성

해석 가능한 회귀 모델을 훈련시키고 복잡한 회귀 모델을 해석하기

선형 모델, 결정 트리, 일반화된 가산 모델과 같은 본래 해석 가능한 회귀 모델을 사용하거나 본래 해석할 수 없는 복잡한 회귀 모델을 해석하기 위한 해석 가능성 특징을 사용하십시오.

회귀 모델을 해석하는 방법을 알아보려면Interpret Machine Learning Models항목을참조하십시오。

함수

`lime`	Local interpretable model-agnostic explanations (LIME)
`适合`	Fit simple model of local interpretable model-agnostic explanations (LIME)
`plot`	Plot results of local interpretable model-agnostic explanations (LIME)

`shapley`	Shapley values
`适合`	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

`适合lm`	선형 회귀 모델 피팅하기
`适合rgam`	Fit generalized additive model (GAM) for regression
`适合rlinear`	Fit linear regression model to high-dimensional data
`适合rtree`	Fit binary decision tree for regression

`LinearModel`	선형 회귀 모델
`RegressionGAM`	Generalized additive model (GAM) for regression
`RegressionLinear`	Linear regression model for high-dimensional data
`RegressionTree`	Regression tree

Interpret Machine Learning Models
Explain model predictions usinglime,shapley, andplotPartialDependence.
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.

Train Linear Regression Model
Train a linear regression model using适合lmto 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.