Ensemble Learning Methods Sample Clauses

Ensemble Learning Methods. Ensemble learning is primarily used to improve the accuracy of the classification model, and to reduce the likelihood of a poor model being selected. The ways to use ensemble models are investigated based on the idea that using multiple models will provide better predictive performance than using any of the constituent models. [Hai] The main ensemble learning methods are: • Bagging Also known as bootstrap aggregating. This involves having each model in the ensemble vote with equal weight. To promote model variance, bagging trains each model in the ensemble with a randomly chosen subset of the training set. • Boosting Boosting involves incrementally building an ensemble by training each new model instance to emphasise the training instances that previous models misclassified. Xxxxxxxx is a general method for improving the accuracy of any given classifier. It is used with weak learners to get better performance by combining the classification results of each learner and to create a single strong learner [Boosting].