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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

by ALEXANDER WAKIM Ramp-up and multi-armed bandits (MAB) are common strategies in online controlled experiments (OCE). These strategies involve changing assignment weights during an experiment. The first is a strategy called ramp-up and is advised by many experts in the field [1].

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

propose a different strategy where the minority class is over-sampled by generating synthetic examples. Their tests are performed using C4.5-generated note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). In their 2002 paper Chawla et al.