Introduction to Random Forest


Time Episode Title Overview
Setup Things to complete before starting this lesson.
00:00 1. Introduction What is a random forest ?
How are random forests used ?
When might I want to use a random forest ?
00:10 2. Decision Trees What is a Decision Tree ?
What are the major drawbacks of Decision Trees ?
00:20 3. Ensemble Learning What is an ensemble method ?
What is bagging ?
What is feature bagging ?
00:30 4. The Random Forest How is a Random Forest an ensemble method ?
How is bootstrap aggregation applied to our decision trees ?
How is feature bagging applied to decision tree modeling ?
00:40 5. Training a Random Forest How do I train a Random Forest in sklearn ?
How do I make a prediction on my trained model ?
How do I create diagnostic graphs to understand how my model is performing ?
00:50 6. Feature Importance How can I determine how important a variable is to the model?
00:55 Finish