Category Archives: Machine Learning

Bagging, Boosting, and Stacking in Machine Learning

Unity is strength

Ensemble learning techniques are quite popular in machine learning. These techniques work by training multiple models and combining their results to get the best possible outcome. In this article, we will learn about three popular ensemble learning methods-bagging, boosting, and stacking. Each one of these methods has its own benefits and limitations, but in practice ensemble methods often… Read More »

Mining Interpretable Rules from Classification Models

Classification Rules

As data scientists, we come across numerous classification problems every once in a while. Ensemble learning techniques like bagging and boosting typically give us quite high classification performances. But all such models are much complex and hard to interpret. To make sure that everything is working fine and also to understand the prediction results/logic better, it becomes necessary… Read More »