Machine Learning: Random Forest with Python from Scratch - Best Slip

The video tutorial explains how to determine the best split in decision trees by implementing a function that evaluates potential questions to ask at each node. The function calculates the Gini impurity and information gain to identify the optimal question that minimizes randomness and maximizes gain. The tutorial walks through the coding process, including initializing variables, iterating over features, and calculating partitions. The goal is to split the dataset into true and false rows, ultimately leading to a leaf node. The lesson concludes with a review of the function's purpose and a preview of the next topic.

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