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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 [11]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[11]를 성장시키려면 회귀 앙상블을 참조하십시오.

분류 학습기지도 기계 학습(Supervised Machine Learning)을 사용하여 데이터를 분류하도록 모델 훈련시키기

함수

모두 확장

templateDiscriminantDiscriminant analysis classifier template
templateECOCError-correcting output codes learner template
templateEnsembleEnsemble learning template
templateKNNk-nearest neighbor classifier template
templateLinearLinear classification learner template
templateNaiveBayesNaive Bayes classifier template
templateSVMSupport vector machine template
templateTreeCreate decision tree template
fitcensembleFit ensemble of learners for classification
predictClassify observations using ensemble of classification models
oobPredictPredict out-of-bag response of ensemble
TreeBagger결정 트리의 배깅 생성하기
fitcensembleFit ensemble of learners for classification
predictPredict responses using ensemble of bagged decision trees
oobPredictEnsemble predictions for out-of-bag observations
fitcecoc서포트 벡터 머신 또는 다른 분류기에 대해 다중클래스 모델 피팅하기
templateSVMSupport vector machine template
predictClassify observations using multiclass error-correcting output codes (ECOC) model

클래스

모두 확장

ClassificationEnsembleEnsemble classifier
CompactClassificationEnsembleCompact classification ensemble class
ClassificationPartitionedEnsembleCross-validated classification ensemble
TreeBaggerBag of decision trees
CompactTreeBaggerCompact ensemble of decision trees grown by bootstrap aggregation
ClassificationBaggedEnsembleClassification ensemble grown by resampling
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

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. (LPBoost and TotalBoost require Optimization Toolbox™.)

Tune RobustBoost

Tune RobustBoost parameters for better predictive accuracy. (RobustBoost requires Optimization Toolbox.)

Surrogate Splits

Gain better predictions when you have missing data by using surrogate splits.

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.