Lecture

Decision Trees: Induction & Attributes

Description

This lecture covers the principles of decision trees, including their construction, attribute selection, and pruning. It explains how decision trees are used for classification tasks, the importance of entropy in attribute selection, and the process of tree induction. The instructor also discusses the bias-variance tradeoff in decision tree models, comparing random forests and boosted trees. Additionally, it explores the concept of ensemble methods in machine learning, such as bagging and stacking. The lecture concludes with insights on model transparency and the challenges of interpreting complex models.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.