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.