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 Random Forest model. It covers the process of implementing feature importance, debugging common errors, and interpreting the results. The tutorial also discusses sorting the features for better readability. Finally, it introduces a future series where viewers will learn to implement machine learning models from scratch without relying on built-in libraries.

What features were mentioned as having importance in the model?

What will the teacher implement in the upcoming videos instead of using built-in libraries?