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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 알아보려면 Interpret Machine Learning Models 항목을 참조하십시오.

함수

모두 확장

LIME(Local Interpretable Model-Agnostic Explanations)

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)

섀플리 값

shapleyShapley values
fitCompute Shapley values for query point
plotPlot Shapley values

부분 종속성

partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
fitlm선형 회귀 모델 피팅하기
fitrgamFit generalized additive model (GAM) for regression
fitrlinearFit linear regression model to high-dimensional data
fitrtreeFit binary decision tree for regression

객체

LinearModel선형 회귀 모델
RegressionGAMGeneralized additive model (GAM) for regression
RegressionLinearLinear regression model for high-dimensional data
RegressionTreeRegression tree

도움말 항목

모델 해석

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.

해석 가능한 모델

Train Linear Regression Model

Train a linear regression model using fitlm 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.