Machine Learning Random Forest with Python from Scratch - Question and Partition

The video tutorial covers the implementation of helper functions and classes for decision trees. It explains how to create a class for questions, implement a match function, and partition data into true and false categories. The tutorial also discusses the recursive nature of tree structures and hints at the importance of recursion in this context. The session concludes with a promise to explore core theoretical concepts in future lectures.

What are the helper functions mentioned, and what purpose do they serve?

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How does recursion relate to the structure of the decision tree discussed in the lecture?

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In what scenarios would you use the methods discussed in the lecture?

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