Ensemble Methods
ML technique where multiple models are trained and used collectively to solve a problem.
Ensemble Methods in ML involve the process of combining several models to solve a single prediction problem. It works by producing multiple models, each working on the same learning task, and then strategically combining their outcomes. Its philosophy is rooted in the idea that a group of weak learners can be combined to form a strong learner. Several methods fall under this category, including Bagging, Boosting and Stacking. Bagging helps to decrease the model’s variance, Boosting helps to decrease the model’s bias, and Stacking combines the prediction of multiple different models together to reduce both bias and variance.
Historically, the first instance of Ensemble Methods dates back to 1990 when multiple classifier systems and voting were used. These methods gained popularity in the 2000s with the success of random forests and boosting algorithms, which demonstrated their effectiveness in improving the accuracy of single model approaches.
Key contributors to the development of Ensemble Methods include Leo Breiman, who introduced the concept of bagging and the random forest; Robert Schapire and Yoav Freund, who developed the theory of Boosting; and David Wolpert, who introduced the idea of Stacking. These individuals made significant contributions in developing and refining the principles and techniques of ensemble learning methods in ML.