Labeled data containing incorrect labels, termed label noise, has gained much attention in machine learning research due to its adverse impact on supervised models. This effort has increased in recent years, as the usage of larger data sets, which are more prone to label noise, has become prevalent. To tackle this problem, studies have explored the sensitivity of the learning process to label noise and devised robust methodologies to overcome it. This talk covers basic concepts in label noise research and explores suggested approaches for overcoming its negative effects. It also showcases two practical examples of easy-to-use methods which were tested on training sets contaminated by label noise and by target value noise.
This talk was held at PyData TLV MeetUp, (April 3rd 19′) by our own Yaniv Katz.
Yaniv Katz is Machine Learning Researcher in the Data Analysis & Algorithms group at SimilarWeb. He is passionate about exploring new fields in machine learning and solving real-world problems using innovative algorithms.