Domino Data Lab

MAY 20, 2021

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. Machine Learning, 57–78.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

AUGUST 1, 2021

but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. Instead, you should focus on how techniques like PDPs and LIME can be used to gain insights into the model’s inner workings and how you can add those to your data science toolbox. See Wei et al. References.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

The Unofficial Google Data Science Blog

JULY 22, 2020

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. Ramp-up solution: measure epoch and condition on its effect If one wants to do full traffic ramp-up and use data from all epochs, they must use an adjusted estimator to get an unbiased estimate of the average reward in each arm.

Experimentation 95

Experimentation Statistics Testing Strategy 95