Ensembling is a technique of combining two or more algorithms of similar or dissimilar types known as base learners. This is done to make a more robust system which incorporates the predictions from all the base learners. In this session, we will take you through the different methods to ensemble base learners in python.
Structure of the Hack-Session
This is a 1-hour Hack- Session and includes the following modules:
- What is Ensemble Learning and benefits?
- Types of Ensemble Learning with example
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- Average
- Vote
- Rank
- Stacking
- Blending
Make sure you don’t miss this hack-session on ensemble learning to improve your model performance! Get your tickets today to access this session.
INSTRUCTOR
Supreeth Manyam
Supreeth Manyam (aka ziron) works with Innovation team at Society Generale (GSC). His interest areas are Machine Learning and Deep Learning. At work, he solves problems such as image enhancement to improve the OCR quality and object recognition.
Apart from work, he loves travel and likes to participate in competitions and have won a few, including #2 in AV Datafest 2017.
Duration of Hack-Session: 1 hour