EP 20 | Duke ML for PMs: Tree Models & Ensembles

What happens when your data doesn't fit neatly into a straight line? We move to Tree Models. This episode explores how algorithms can mimic human decision-making through a series of "If/Then" splits, and how combining them creates incredibly powerful predictive engines.Key Topics:Decision Trees: Understanding roots, nodes, and leaves to visualize exactly how a model reaches its conclusion.Random Forests (Ensemble Learning): Why relying on a "committee" of trees is better than trusting just one, and how it prevents overfitting.Feature Importance: How tree models naturally highlight which data points are actually driving your product's outcomes.Note: This is an AI-generated study resource created via NotebookLM based on Duke University’s ML for Product Managers curriculum and personal study notes.Shutterstock