Nov. 16, 2023, 7:57 p.m. | /u/gykovacs

Machine Learning

I think many of us came across publications with ML performance scores (such as accuracy, sensitivity, etc.) that seemed unrealistic, still getting a lot of citations. We knew they were false, invalid, incorrect, maybe a typo, maybe some bug in the evaluation, maybe cheating, but no one invested the time to reimplement the method and prove it wrong.

However, ML performance scores - especially if multiple ones are reported - cannot take any values independently. There are numerous constraints imposed …

accuracy cheating citations etc evaluation false machinelearning performance publications sensitivity think

Data Engineer

@ Cepal Hellas Financial Services S.A. | Athens, Sterea Ellada, Greece

Senior Manager Data Engineering

@ Publicis Groupe | Bengaluru, India

Senior Data Modeler

@ Sanofi | Hyderabad

VP, Product Management - Data, AI & ML

@ Datasite | USA - MN - Minneapolis

Supervisão de Business Intelligence (BI)

@ Publicis Groupe | São Paulo, Brazil

Data Manager Advertising (f|m|d) (80-100%) - Zurich - Hybrid Work

@ SMG Swiss Marketplace Group | Zürich, Switzerland