도움말 센터 도움말 센터

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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 학습기

머신러닝 지도 학습을 사용하여 데이터를 분류하도록 모델 훈련시키기

블록

ClassificationEnsemble Predict

Classify observations using ensemble of decision trees

함수

학습기 템플릿 만들기

`templateDiscriminant`	Discriminant analysis classifier template
`templateECOC`	Error-correcting output codes learner template
`templateEnsemble`	Ensemble learning template
`templateKNN`	k-nearest neighbor classifier template
`templateLinear`	Linear classification learner template
`templateNaiveBayes`	Naive Bayes classifier template
`templateSVM`	Support vector machine template
`templateTree`	Create decision tree template

분류 앙상블

분류 앙상블 만들기

`fitcensemble`	Fit ensemble of learners for classification
`compact`	Compact classification ensemble

분류 앙상블 수정하기

`resume`	Resume training ensemble
`removeLearners`	Remove members of compact classification ensemble

분류 앙상블 해석하기

`lime`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`predictorImportance`	Estimates of predictor importance for classification ensemble of decision trees
`shapley`	Shapley values

분류 앙상블 교차 검증하기

`crossval`	Cross-validate ensemble
`kfoldEdge`	Classification edge for cross-validated classification model
`kfoldLoss`	Classification loss for cross-validated classification model
`kfoldMargin`	Classification margins for cross-validated classification model
`kfoldPredict`	Classify observations in cross-validated classification model
`kfoldfun`	Cross-validate function for classification

성능 측정하기

`loss`	Classification error
`resubLoss`	Classification error by resubstitution
`compareHoldout`	Compare accuracies of two classification models using new data
`edge`	Classification edge
`margin`	Classification margins
`resubEdge`	Classification edge by resubstitution
`resubMargin`	Classification margins by resubstitution
`testckfold`	Compare accuracies of two classification models by repeated cross-validation

관측값 분류하기

`predict`	Classify observations using ensemble of classification models
`resubPredict`	Classify observations in ensemble of classification models
`oobPredict`	Predict out-of-bag response of ensemble

분류 앙상블의 속성 수집하기

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU

배깅 분류 트리

`fitcensemble`	Fit ensemble of learners for classification
`TreeBagger`	배깅 결정 트리의 앙상블
`predict`	Predict responses using ensemble of bagged decision trees
`oobPredict`	Ensemble predictions for out-of-bag observations

다중클래스 ECOC

ECOC 만들기

`fitcecoc`	서포트 벡터 머신 또는 다른 분류기에 대해 다중클래스 모델 피팅하기
`compact`	Reduce size of multiclass error-correcting output codes (ECOC) model

ECOC 수정하기

discardSupportVectors Discard support vectors of linear SVM binary learners in ECOC model

ECOC 해석하기

`lime`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values

ECOC 교차 검증하기

`crossval`	Cross-validate multiclass error-correcting output codes (ECOC) model
`kfoldEdge`	Classification edge for cross-validated ECOC model
`kfoldLoss`	Classification loss for cross-validated ECOC model
`kfoldMargin`	Classification margins for cross-validated ECOC model
`kfoldPredict`	Classify observations in cross-validated ECOC model
`kfoldfun`	Cross-validate function using cross-validated ECOC model

성능 측정하기

`loss`	Classification loss for multiclass error-correcting output codes (ECOC) model
`resubLoss`	Resubstitution classification loss for multiclass error-correcting output codes (ECOC) model
`compareHoldout`	Compare accuracies of two classification models using new data
`edge`	Classification edge for multiclass error-correcting output codes (ECOC) model
`margin`	Classification margins for multiclass error-correcting output codes (ECOC) model
`resubEdge`	Resubstitution classification edge for multiclass error-correcting output codes (ECOC) model
`resubMargin`	Resubstitution classification margins for multiclass error-correcting output codes (ECOC) model
`testckfold`	Compare accuracies of two classification models by repeated cross-validation

관측값 분류하기

`predict`	Classify observations using multiclass error-correcting output codes (ECOC) model
`resubPredict`	Classify observations in multiclass error-correcting output codes (ECOC) model

ECOC의 속성 수집하기

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU

클래스

분류 앙상블

`ClassificationEnsemble`	Ensemble classifier
`CompactClassificationEnsemble`	Compact classification ensemble class
`ClassificationPartitionedEnsemble`	Cross-validated classification ensemble

배깅 분류 트리

`TreeBagger`	배깅 결정 트리의 앙상블
`CompactTreeBagger`	Compact ensemble of bagged decision trees
`ClassificationBaggedEnsemble`	Classification ensemble grown by resampling

다중클래스 ECOC

`ClassificationECOC`	Multiclass model for support vector machines (SVMs) and other classifiers
`CompactClassificationECOC`	Compact multiclass model for support vector machines (SVMs) and other classifiers
`ClassificationPartitionedECOC`	Cross-validated multiclass ECOC model for support vector machines (SVMs) and other classifiers

도움말 항목

Train Ensemble Classifiers Using Classification Learner App
Create and compare ensemble classifiers, and export trained models to make predictions for new data.
Framework for Ensemble Learning
Obtain highly accurate predictions by using many weak learners.
Ensemble Algorithms
Learn about different algorithms for ensemble learning.
Train Classification Ensemble
Train a simple classification ensemble.
Test Ensemble Quality
Learn methods to evaluate the predictive quality of an ensemble.
Handle Imbalanced Data or Unequal Misclassification Costs in Classification Ensembles
Learn how to set prior class probabilities and misclassification costs.
Classification with Imbalanced Data
Use the RUSBoost algorithm for classification when one or more classes are over-represented in your data.
LPBoost and TotalBoost for Small Ensembles
Create small ensembles by using the LPBoost and TotalBoost algorithms.
Tune RobustBoost
Tune RobustBoost parameters for better predictive accuracy.
Surrogate Splits
Gain better predictions when you have missing data by using surrogate splits.
Train Classification Ensemble in Parallel
Train a bagged ensemble in parallel reproducibly.
Bootstrap Aggregation (Bagging) of Classification Trees Using TreeBagger
Create a TreeBagger ensemble for classification.
Credit Rating by Bagging Decision Trees
This example shows how to build an automated credit rating tool.
Random Subspace Classification
Increase the accuracy of classification by using a random subspace ensemble.
Predict Class Labels Using ClassificationEnsemble Predict Block
Train a classification ensemble model with optimal hyperparameters, and then use the ClassificationEnsemble Predict block for label prediction.