The Unofficial Google Data Science Blog

JANUARY 14, 2016

For this purpose, let’s assume we use a t-test for difference between group means. Effect size thus defined is useful because the statistical power of a classical test for $delta$ being non-zero depends on $e/sqrt{tilde{n}}$, where $tilde{n}$ is the harmonic mean of sample sizes of the two groups being compared.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

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

MAY 20, 2021

Their tests are performed using C4.5-generated 1988), E-state data (Hall et al., This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Data mining for direct marketing: Problems and solutions. Chawla et al., Pima Indian Diabetes (Smith et al., Quinlan, J.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

One way to check $f_theta$ is to gather test data and check whether the model fits the relationship between training and test data. This tests the model’s ability to distinguish what is common for each item between the two data sets (the underlying $theta$) and what is different (the draw from $f_theta$).

KDD 40

KDD Testing Machine Learning Modeling 40

Domino Data Lab

AUGUST 1, 2021

For this demo we’ll use the freely available Statlog (German Credit Data) Data Set, which can be downloaded from Kaggle. This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. After forming the X and y variables, we split the data into training and test sets.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139