A classification ensemble is a predictive model composed of a weighted combination of multiple classification models. In general, combining multiple classification models increases predictive performance.
To explore classification ensembles interactively, use the Classification Learner app. For greater flexibility, use fitcensemble in the command-line interface to boost or bag classification trees, or to grow a random forest [11]. For details on all supported ensembles, see Ensemble Algorithms. To reduce a multiclass problem into an ensemble of binary classification problems, train an error-correcting output codes (ECOC) model. For details, see fitcecoc.
To boost regression trees using LSBoost, or to grow a random forest of regression trees[11], see Regression Ensembles.
| Classification Learner | Train models to classify data using supervised machine learning |
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.
Learn about different algorithms for ensemble learning.
Train a simple classification ensemble.
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 parameters for better predictive accuracy. (RobustBoost requires Optimization Toolbox.)
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.