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

`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

`fitlm`	선형 회귀 모델 피팅하기
`fitrgam`	Fit generalized additive model (GAM) for regression
`fitrlinear`	Fit linear regression model to high-dimensional data
`fitrtree`	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 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.
Interpret Regression Models Trained in Regression Learner App
Determine how features are used in trained regression models by using partial dependence plots.

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.