Data mining, Knowledge Discovery, Publishing and Risk

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Data mining for direct marketing: Problems and solutions. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Morgan Kaufmann Publishers Inc.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Changing assignment weights with time-based confounders

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

Experimentation Statistics Testing Strategy

Data Leaders Brief

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Changing assignment weights with time-based confounders

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