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分类集成

用于进行多类学习的提升、随机森林、装袋、随机子空间和 ECOC 集成

分类集成是由多个分类模型的加权组合构成的预测模型。一般来讲,将多个分类模型相结合可以提高预测性能。

要以交互方式研究分类集成,可以使用 Classification Learner App。为了获得更大的灵活性,可以在命令行界面中使用 fitcensemble 来提升或装袋分类树,或者生成随机森林 [11]。有关支持的所有集成的详细信息,请参阅Ensemble Algorithms。要将多类问题简化为二分类问题的集成,可以训练纠错输出编码 (ECOC) 模型。有关详细信息,请参阅 fitcecoc

要使用 LSBoost 提升回归树,或者要生成回归树的随机森林 [11],请参阅回归集成

App

Classification LearnerTrain models to classify data using supervised machine learning

函数

全部展开

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
TreeBaggerCreate bag of decision trees
fitcensembleFit ensemble of learners for classification
predictPredict responses using ensemble of bagged decision trees
oobPredictEnsemble predictions for out-of-bag observations
fitcecocFit multiclass models for support vector machines or other classifiers
templateSVMSupport vector machine template
predictClassify observations using multiclass error-correcting output codes (ECOC) model

全部展开

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

主题

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.