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 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

These estimates can be useful to make risk-adjusted decisions and explore-exploit trade-offs, or to find situations where the underlying regression method is particularly good or bad. References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2]

KDD 40

KDD Testing Machine Learning Modeling 40

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. 2015) for additional details. Conference on Knowledge Discovery and Data Mining, pp.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139