Why Do Tree Ensembles Work?

Explore the statistical properties and effectiveness of decision tree ensembles in this 41-minute conference talk from Strange Loop. Delve into the power of random forest and AdaBoost algorithms for classification and regression tasks. Begin with an overview of these ensemble methods before examining their effectiveness through generic arguments like bias-variance decomposition and Hoeffding's inequality. Progress to more advanced topics, including the interpretation of random forests as kernel machines and the significance of margin in these models. Gain insights from Joe Ross, a PhD mathematician and experienced data scientist, as he bridges the gap between theoretical understanding and practical applications of tree ensembles in machine learning. Suitable for those with a background in supervised learning and statistical concepts, this talk offers a comprehensive look at why tree ensembles are such powerful tools in data analysis and prediction.