해석 가능성

해석 가능한 분류 모델을 훈련시키고 복잡한 분류 모델을 해석하기

선형 모델, 결정 트리, 일반화된 가산 모델처럼 본질적으로 해석 가능한 분류 모델을 사용하거나 아니면 해석 가능성 특징을 사용하여 원래 해석이 불가능한 복잡한 분류 모델을 해석합니다.

분류 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

`fitcgam`	Fit generalized additive model (GAM) for binary classification (R2021a 이후)
`fitclinear`	Fit binary linear classifier to high-dimensional data
`fitctree`	다중클래스 분류를 위한 이진 결정 트리 피팅

`ClassificationGAM`	Generalized additive model (GAM) for binary classification (R2021a 이후)
`ClassificationLinear`	Linear model for binary classification of high-dimensional data
`ClassificationTree`	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.
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 Classifiers Trained in Classification Learner App
To understand how trained classifiers 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 Classifiers Trained in Classification Learner App
Determine how features are used in trained classifiers by creating 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.
최근접이웃을 사용한 분류
다양한 거리 측정법을 사용하여 훈련 데이터 세트에 포함된 점까지의 거리를 기준으로 데이터 점을 분류합니다.