Ensemble Learning in ML – Part 3: Stacking

Introduction In the previous posts, we discussed Bagging and Boosting ensemble learning in ML and how they are useful. We also discussed the algorithms which are based on it i.e. Ada Boost and Gradient Boosting. In this part, we will discuss another ensemble learning technique known as Stacking. We also discuss a bit about Blending … Continue reading Ensemble Learning in ML – Part 3: Stacking

Ensemble Learning in ML – Part 2: Boosting

Introduction In the last part, we discussed what ensembling learning in ML is and how it is useful. We also discussed one ensemble learning technique - Bagging - and algorithms that are based on it i.e. Bagging meta-estimator and Random Forest. In this part, we will discuss another ensemble learning technique, which is known as … Continue reading Ensemble Learning in ML – Part 2: Boosting

Ensemble Learning in ML – Part 1: Bagging

Introduction  Let’s understand ensemble learning with an example. Suppose you have a startup idea and you wanted to know whether that idea is good to move ahead with it or not. Now, you want to take preliminary feedback on it before committing money and your precious time to it. So you may ask one of … Continue reading Ensemble Learning in ML – Part 1: Bagging