[PyData] Learning with Label Noise - Yaniv Katz

PyData Tel Aviv Meetup #22 3 April 2019 Sponsored and Hosted by SimilarWeb https://www.meetup.com/PyData-Tel-Aviv/ 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. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. Find a PyData chapter near you: meetup.com/pro/pydata