by Rahul Chandnani | Publisher: Technics Publications | Release Date: January 2018 | ISBN: 9781634623216

This video explains ensemble learning along with its use cases and variations. There are six clips in this video: Ensemble Learning Overview. This clip explains ensemble learning and the three main reasons why this paradigm is so important. Ensemble learning is the best way to improve the performance of a single machine learning algorithm. Learn the necessary prerequisites of base learners for ensemble learning. Drawbacks of Single Learner. This clip explores the limitations of a single classifier and how these limitations can be tackled by ensemble learning. See how statistical learning and the representational problem could be tackled by ensemble learning. Types of Ensemble Methods. This clip provides an overview to both Homogeneous Ensemble Learning and Heterogeneous Ensemble Learning. Heterogeneous Ensemble Methods. This clip covers the two types of heterogeneous ensemble learning which are Stacking and Cascade Generalization. Homogeneous Ensemble Methods. This clip covers the two types of homogeneous ensemble learning methods which are bagging and boosting. Adaboost (Adaptive Boosting). This clip covers Adaboost in detail including the Adaboost Algorithm, Theoretical Guarantees on training error, the expected and observed behavior of Adaboost, and its advantages and disadvantages.

1. Ensemble Learning Overview 00:06:33
2. Drawbacks of Single Learner 00:03:13
3. Types of Ensemble Methods 00:01:54
4. Heterogeneous Ensemble Methods 00:03:03
5. Homogeneous Ensemble Methods 00:10:53
6. Adaboost (Adaptive Boosting) 00:38:49