Ensemble Learning to Improve Machine Learning Results

Ensemble learning helps improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model. That is why ensemble methods placed first in many prestigious machine learning competitions, such as the Netflix Competition, KDD 2009, and Kaggle. The Statsbot team wanted to give you the advantage of this approach and asked a data scientist, Vadim Smolyakov, to dive into three basic ensemble learning techniques. Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).