Domino Data Lab

MAY 20, 2021

This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). Chawla et al. Indeed, in the original paper Chawla et al. Cost, S., & Salzberg, S.

Machine Learning 136

Machine Learning Metrics Data mining Data Science 136

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

Domino Data Lab

AUGUST 1, 2021

This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. This is to be expected, as there is no reason for a perfect 50:50 separation of the good vs. bad credit risk. In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), pages 24–30, Melbourne, Australia, 2017.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139