The ensemble is a method of combining a diverse set of learners together to improvise on the stability and predictive power of the model. Two types of Ensemble learning methods are :
* Bagging : Bagging method helps you to implement similar learners on small sample populations. It helps you to make nearer predictions.
* Boosting : Boosting is an iterative method which allows you to adjust the weight of an observation depends upon the last classification. Boosting decreases the bias error and helps you to build strong predictive models.
