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. For example, imagine a fantasy football site is considering displaying advanced player statistics.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

MAY 20, 2021

This renders measures like classification accuracy meaningless. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. The use of multiple measurements in taxonomic problems. Chawla et al. Indeed, in the original paper Chawla et al. Machine Learning, 57–78. Quinlan, J.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Well, it turns out that depending on what it cares to measure, an LSOS might not have enough data. The practical consequence of this is that we can’t afford to be sloppy about measuring statistical significance and confidence intervals. Being dimensionless, it is a simple measure of the variability of a (non-negative) random variable.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

Posteriors are useful to understand the system, measure accuracy, and make better decisions. Methods like the Poisson bootstrap can help us measure the variability of $t$, but don’t give us posteriors either, particularly since good high-dimensional estimators aren’t unbiased.

KDD 40

KDD Testing Machine Learning Modeling 40