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분류 앙상블

다중클래스 학습을 위한 부스팅, 랜덤 포레스트, 배깅, 랜덤 부분공간, ECOC 앙상블

분류 앙상블은 여러 분류 모델의 가중 조합으로 구성된 예측 모델입니다. 일반적으로, 여러 분류 모델을 조합하면 예측 성능이 높아집니다.

분류 앙상블을 대화형 방식으로 살펴보려면 분류 학습기 앱을 사용하십시오. 명령줄 인터페이스에서 fitcensemble을 사용하여 분류 트리를 부스팅 또는 배깅하거나 랜덤 포레스트 [12]를 성장시켜 유연성을 높일 수 있습니다. 지원되는 모든 앙상블에 대한 자세한 내용은 Ensemble Algorithms 항목을 참조하십시오. 다중클래스 문제를 이진 분류 문제 앙상블로 줄이려면 오류 수정 출력 코드(ECOC) 모델을 훈련시키십시오. 자세한 내용은 fitcecoc를 참조하십시오.

LSBoost를 사용하여 회귀 트리를 부스팅하거나 회귀 트리의 랜덤 포레스트[12]를 성장시키려면 회귀 앙상블을 참조하십시오.

앱

분류 학습기머신러닝 지도 학습을 사용하여 데이터를 분류하도록 모델 훈련시키기

블록

ClassificationEnsemble PredictClassify observations using ensemble of decision trees (R2021a 이후)
ClassificationECOC PredictClassify observations using error-correcting output codes (ECOC) classification model (R2023a 이후)
IncrementalClassificationECOC PredictClassify observations using incremental ECOC classification model (R2024a 이후)

함수

모두 확장

templateDiscriminantDiscriminant analysis classifier template
templateECOCError-correcting output codes learner template
templateEnsembleEnsemble learning template
templateKNNk-nearest neighbor classifier template
templateLinearLinear learner template
templateNaiveBayesNaive Bayes classifier template
templateSVMSupport vector machine template
templateTreeCreate decision tree template

분류 앙상블 만들기

fitcensembleFit ensemble of learners for classification
compactReduce size of classification ensemble model

분류 앙상블 수정하기

resumeResume training of classification ensemble model
removeLearnersRemove members of compact classification ensemble

분류 앙상블 해석하기

limeLocal interpretable model-agnostic explanations (LIME) (R2020b 이후)
partialDependenceCompute partial dependence (R2020b 이후)
permutationImportancePredictor importance by permutation (R2024a 이후)
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predictorImportanceEstimates of predictor importance for classification ensemble of decision trees
shapleyShapley values (R2021a 이후)

분류 앙상블 교차 검증하기

crossvalCross-validate machine learning model
kfoldEdgeClassification edge for cross-validated classification model
kfoldLossClassification loss for cross-validated classification model
kfoldMarginClassification margins for cross-validated classification model
kfoldPredictClassify observations in cross-validated classification model
kfoldfunCross-validate function for classification

성능 측정하기

lossClassification loss for classification ensemble model
resubLossResubstitution classification loss for classification ensemble model
compareHoldoutCompare accuracies of two classification models using new data
edgeClassification edge for classification ensemble model
marginClassification margins for classification ensemble model
resubEdgeResubstitution classification edge for classification ensemble model
resubMarginResubstitution classification margins for classification ensemble model
testckfoldCompare accuracies of two classification models by repeated cross-validation

관측값 분류하기

predictPredict labels using classification ensemble model
resubPredictClassify observations in classification ensemble by resubstitution
oobPredictPredict out-of-bag labels and scores of bagged classification ensemble

분류 앙상블의 속성 수집하기

gatherGather properties of Statistics and Machine Learning Toolbox object from GPU (R2020b 이후)
fitcensembleFit ensemble of learners for classification
TreeBagger배깅 결정 트리의 앙상블
predictPredict responses using ensemble of bagged decision trees
oobPredictEnsemble predictions for out-of-bag observations

ECOC 만들기

fitcecoc서포트 벡터 머신 또는 다른 분류기에 대해 다중클래스 모델 피팅하기
compactReduce size of multiclass error-correcting output codes (ECOC) model

ECOC 수정하기

discardSupportVectorsDiscard support vectors of linear SVM binary learners in ECOC model

ECOC 해석하기

limeLocal interpretable model-agnostic explanations (LIME) (R2020b 이후)
partialDependenceCompute partial dependence (R2020b 이후)
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
shapleyShapley values (R2021a 이후)

ECOC 교차 검증하기

crossvalCross-validate machine learning model
kfoldEdgeClassification edge for cross-validated ECOC model
kfoldLossClassification loss for cross-validated ECOC model
kfoldMarginClassification margins for cross-validated ECOC model
kfoldPredictClassify observations in cross-validated ECOC model
kfoldfunCross-validate function using cross-validated ECOC model

성능 측정하기

lossClassification loss for multiclass error-correcting output codes (ECOC) model
resubLossResubstitution classification loss for multiclass error-correcting output codes (ECOC) model
compareHoldoutCompare accuracies of two classification models using new data
edgeClassification edge for multiclass error-correcting output codes (ECOC) model
marginClassification margins for multiclass error-correcting output codes (ECOC) model
resubEdgeResubstitution classification edge for multiclass error-correcting output codes (ECOC) model
resubMarginResubstitution classification margins for multiclass error-correcting output codes (ECOC) model
testckfoldCompare accuracies of two classification models by repeated cross-validation

관측값 분류하기

predictClassify observations using multiclass error-correcting output codes (ECOC) model
resubPredictClassify observations in multiclass error-correcting output codes (ECOC) model

ECOC의 속성 수집하기

gatherGather properties of Statistics and Machine Learning Toolbox object from GPU (R2020b 이후)

객체

모두 확장

ClassificationEnsembleEnsemble classifier
CompactClassificationEnsembleCompact classification ensemble
ClassificationPartitionedEnsembleCross-validated classification ensemble
TreeBagger배깅 결정 트리의 앙상블
CompactTreeBaggerCompact ensemble of bagged decision trees
ClassificationBaggedEnsembleClassification ensemble grown by resampling

클래스

모두 확장

ClassificationECOCMulticlass model for support vector machines (SVMs) and other classifiers
CompactClassificationECOCCompact multiclass model for support vector machines (SVMs) and other classifiers
ClassificationPartitionedECOCCross-validated multiclass ECOC model for support vector machines (SVMs) and other classifiers

도움말 항목