Focal loss vs binary cross entropy a practical guide for imbalanced classification

7 Techniques to Handle Imbalanced Data - KDnuggets

This blog post introduces seven techniques that are commonly applied in domains like intrusion detection or real-time bidding, because the datasets are often extremely imbalanced.

scikit-learn-contrib/imbalanced-learn: A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning - scikit-learn-contrib/imbalanced-learn

Handling imbalanced dataset in supervised learning using family of SMOTE algorithm. - DataScienceCentral.com

Consider a problem where you are working on a machine learning classification problem. You get an accuracy of 98% and you are very happy. But that happiness doesn’t last long when you look at the confusion matrix and realize that majority class is 98% of the total data and all…

How to Handle Imbalanced Classes in Machine Learning

Imbalanced classes put "accuracy" out of business. This is a surprisingly common problem in machine learning, and this guide shows you how to handle it.