(IS48) Decision Trees and Random Forests

Starting with the basics, we introduce what decision trees are and how they can be used to make predictions and classify data. Learn how to build and analyze decision trees using measures like Gini impurity, Shannon entropy, and information gain, which help in choosing the best splits based on data purity. We then explore the effects of different hierarchies of predictors on the performance and results of decision trees. The video progresses to discuss random forests—robust ensembles of decision trees—detailing how they are constructed using bootstrapping techniques to enhance model accuracy and prevent overfitting. By aggregating multiple trees, random forests improve prediction stability and accuracy over individual trees.

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