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) (R2020b 이후)
`fit`	Fit simple model of local interpretable model-agnostic explanations (LIME) (R2020b 이후)
`plot`	Plot results of local interpretable model-agnostic explanations (LIME) (R2020b 이후)

`shapley`	Shapley values (R2021a 이후)
`fit`	Compute Shapley values for query points (R2021a 이후)
`plot`	Plot Shapley values using bar graphs (R2021a 이후)
`boxchart`	Visualize Shapley values using box charts (box plots) (R2024a 이후)
`swarmchart`	Visualize Shapley values using swarm scatter charts (R2024a 이후)

`partialDependence`	Compute partial dependence (R2020b 이후)
`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 (R2021a 이후)
`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 (R2021a 이후)
`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.
Shapley Output Functions
Stop Shapley computations, create plots, save information to your workspace, or perform calculations while using shapley.
Introduction to Feature Selection
Learn about feature selection algorithms and explore the functions available for feature selection.
Explain Model Predictions for Regression Models Trained in Regression Learner App
To understand how trained regression models use predictors to make predictions, use global and local interpretability tools, such as partial dependence plots, LIME values, and Shapley values.
Use Partial Dependence Plots to Interpret Regression Models Trained in Regression Learner App
Determine how features are used in trained regression models by creating 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.