Random Forest: From Decision Trees to Ensemble Learning

This lecture introduces Random Forest, an ensemble learning algorithm that combines multiple Decision Trees for reliable predictions. It explains bootstrap sampling, random feature selection, and the working of Random Forest. The presentation also covers hyperparameters, Out-of-Bag (OOB) evaluation, and feature importance. A Python implementation is included to demonstrate practical use of the algorithm.

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