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Ensembles de clasificación

Potenciación, bosque aleatorio, empaquetado, subespacio aleatorio y ensembles ECOC para aprendizaje multiclase

Un ensemble de clasificación es un modelo predictivo compuesto por una combinación ponderada de varios modelos de clasificación. En general, la combinación de varios modelos de clasificación aumenta la capacidad predictiva.

Para explorar ensembles de clasificación de forma interactiva, utilice la app Classification Learner. Para mayor flexibilidad, utilice fitcensemble en la interfaz de línea de comandos para potenciar o empaquetar árboles de clasificación o aumentar un bosque aleatorio [12]. Para obtener información sobre todos los ensembles compatibles, consulte Ensemble Algorithms. Para reducir un problema multiclase a un ensemble de problemas de clasificación binaria, entrene un modelo de códigos de salida de corrección de errores (ECOC, por sus siglas en inglés). Para obtener más detalles, consulte fitcecoc.

Para potenciar árboles de regresión mediante LSBoost o aumentar un bosque aleatorio de árboles de regresión[12], consulte Ensembles de regresión.

Apps

Classification LearnerTrain models to classify data using supervised machine learning

Bloques

ClassificationEnsemble PredictClassify observations using ensemble of decision trees

Funciones

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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

Clases

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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

Temas

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.

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.

Documentación de Statistics and Machine Learning Toolbox

Soporte

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

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