Workshop 08 | Machine Learning: Zero to Hero The decision tree builds regression or classification models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. Random Forest Regression is a supervised learning algorithm that uses ensemble learning methods for regression. The ensemble learning method is a technique that combines predictions from multiple machine learning algorithms to make a more accurate prediction than a single model. In this detailed workshop, we will understand the core concepts of Tree-based ML Models and will implement both techniques, Decision Tree & Random Forest Regression.
Thursday, October 8, 2020
3:00 PM β 4:30 PM UTC
3:00 PM | Introduction to Tree Based Regression |
3:20 PM | Introduction Decision Tree |
3:25 PM | Python Implementation of Decision Tree |
3:35 PM | Introduction to Random Forest |
3:40 PM | Python Implementation of Random Forest |
4:00 PM | Recap & Comparison |
4:10 PM | Q/A |
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