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Conjuntos de clasificación

Refuerzo, bosque al azar, embolsado, subespacio al azar, y conjuntos ECOC para el aprendizaje multiclase

Un conjunto de clasificación es un modelo predictivo compuesto por una combinación ponderada de múltiples modelos de clasificación. En general, la combinación de múltiples modelos de clasificación aumenta el rendimiento predictivo.

Para explorar conjuntos de clasificación de forma interactiva, utilice la aplicación Estudiante de clasificación. Para mayor flexibilidad, utilice fitcensemble en la interfaz de línea de comandos para aumentar o empaquetar árboles de clasificación, o para cultivar un [11] de bosque aleatorio. Para obtener información detallada sobre todos los conjuntos compatibles, consulte Ensemble Algorithms. Para reducir un problema de multiclase en un conjunto de problemas de clasificación binaria, entrene un modelo de códigos de salida de corrección de errores (ECOC). Para obtener más información, consulte fitcecoc.

Para impulsar árboles de regresión usando LSBoost, o para cultivar un bosque aleatorio de árboles de regresión[11], ver Conjuntos de regresión.

Aplicaciones

Estudiante de clasificaciónTrain models to classify data using supervised machine learning

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
predictPredict labels 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
predictPredict labels using multiclass, error-correcting output codes 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 or other classifiers
CompactClassificationECOCCompact multiclass model for support vector machines or 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.

Classification with Many Categorical Levels

Train an ensemble of classification trees using data containing predictors with many categorical levels.

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

Create a TreeBagger ensemble for classification.

Random Subspace Classification

Increase the accuracy of classification by using a random subspace ensemble.

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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