Lecture

Binary Classification by Regression: Decision Functions and Cost Functions

Description

This lecture covers the concept of binary classification by regression, focusing on decision functions and cost functions. It explains how to regress labels to determine a decision function, the quality of this approximation, and different loss functions such as 0/1 cost, logistic cost, quadratic cost, and hinge error. The instructor also discusses the implications of these cost functions on linear regression for binary classification.

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.