Data Science Basics: An Introduction to Ensemble Learners

Algorithm selection can be challenging for machine learning newcomers. Often when building classifiers, especially for beginners, an approach is adopted to problem solving which considers single instances of single algorithms. However, in a given scenario, it may prove more useful to chain or group classifiers together, using the techniques of voting, weighting, and combination to pursue the most accurate classifier possible. Ensemble learners are classifiers which provide this functionality in a variety of ways. This post will provide an overview of bagging, boosting, and stacking, arguably the most used and well-known of the basic ensemble methods.