Machine Learning: Random Forest with Python from Scratch - Feature Importance

The video tutorial explains how to determine the importance of features in a dataset using a trained model, specifically a random forest classifier. It covers the process of calculating feature importance, debugging common errors, and interpreting the results. The tutorial also hints at future lessons where the instructor will teach how to implement these functions from scratch without relying on built-in libraries like SK Learn.

What new insight or understanding did you gain from this video?