Machine Learning Random Forest with Python from Scratch - How to Classify

The video tutorial covers the process of training a model using recursive calls, focusing on decision and leaf nodes. It explains the implementation of the classify method, which determines predictions based on node questions. The tutorial includes running the code, addressing common errors, and testing the model. It concludes with a discussion on future improvements, such as writing helper methods and an accuracy function to evaluate model performance.

What is the purpose of the 'fit' method in the context of the decision tree?

Explain the difference between a leaf node and a decision node in a decision tree.

What happens when the node is a leaf node during the classification process?

Describe the recursive process used to classify a row in the decision tree.

What are the common errors encountered while implementing the decision tree code?

How does the classify method determine the output for a given input?

What additional methods are suggested to improve the output of the decision tree?