A Primer to Ensemble Learning – Bagging and Boosting

Ensemble is a machine learning concept in which multiple models are trained using the same learning algorithm. Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. Boosting is an iterative technique which adjusts the weight of an observation based on the last classification. If an observation was classified incorrectly, it tries to increase the weight of this observation. Boosting in general builds strong predictive models.