Knowledge Discovery, Measurement and Strategy

Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

JULY 22, 2020

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

MAY 20, 2021

This renders measures like classification accuracy meaningless. propose a different strategy where the minority class is over-sampled by generating synthetic examples. The use of multiple measurements in taxonomic problems. The class imbalance problem: Significance and strategies. In their 2002 paper Chawla et al.

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Changing assignment weights with time-based confounders

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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