Module 2: Random Forests
Module Overview
In this module, you will learn about Random Forests, a powerful ensemble learning technique that builds upon the foundation of decision trees. Random Forests combine multiple decision trees to create a more robust, accurate, and stable model that mitigates many of the limitations of individual decision trees.
You'll learn how Random Forests work, their advantages over single decision trees, and how to implement and tune them effectively using scikit-learn. This module will also introduce you to the concept of ensemble learning and its benefits in machine learning.
Learning Objectives
- Understand how categorical encoding effects tree based models differently
- Ordinal Encoding of High Cardinality Categoricals
- Understand how tree based ensembles reduce overfitting
- Build & Interpret Random Forests using scikit learn
Guided Project
Open JDS_SHR_222_guided_project_notes.ipynb in the GitHub repository below to follow along with the guided project:
Guided Project Video
Module Assignment
Complete the Module 2 assignment to practice Random Forest techniques you've learned.
It's time to apply what you've learned about Random Forests to improve your predictions in the Kaggle competition!