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.