The first annual MEBDI Machine Learning Competition sponsored by Litigation Analytics was held in 2020.
The objective of this year’s competition was to devise a machine learning algorithm with the best out-of-sample prediction performance for individual annual earnings for a sample drawn from the 2012 US Current Population Survey. Further details and competition rules can be found here: 2020 MEBDI ML Competition Details
Submissions were judged by a two-judge faculty panel with one internal member (Professor Joe Mullins) and one outside member (Professor Elena Manresa, NYU Econ), who reviewed the submissions and ran the source codes to verify the results. We are very grateful to Joe and Elena for their invaluable help.
This year’s prizes were funded by Litigation Analytics, Inc., whose support is gratefully acknowledged.
Part II (Grand Prize, $4,000) awarded to the team of Dhananjay Ghei and Sang Min Lee
The 2021 MEBDI ML Competition will be announced later in the Fall.