An ensemble methods are technique which uses multiple independent similar or different models and then combine them to produce improved results. Ensemble methods usually produces more accurate solutions than a single model would. The motivation for using ensemble models is to reduce bias or variance.
For e.g. A random forest is an ensemble of multiple decision trees.
The most popular ensemble methods are boosting, bagging, and stacking. Ensemble methods are ideal for regression and classification, where they reduce bias and variance to boost the accuracy of models.
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in
There are 2 main advantages of using ensemble methods. • Ensemble methods help to reduce bias or variance. • There are a number of models (e.g. decision tree, logistic regression) you can use, but at the beginning you do not know which ones are good or bad. So you use all of them and aggregate their predictions. On the fly you observe which models are better.