This repo corresponds to a blog post I wrote discussing how to learn from data with imbalanced classes. The blog post is here: http://www.svds.com/learning-imbalanced-classes/. In this directory you'll find two Python Jupyter notebooks illustrating two points made in that blog post.
Gaussians.ipynb is an interactive notebook that allows you to play with
varied sampling from two Gaussian distributions to see what logistic
regression does with the points as the mixtures are varied.
ImbalancedClasses.ipynb illustrates a method called blagging (basically,
downsampled bagging) on a domain called
Glass from the
UCI Repository. It goes through steadily more imbalanced versions of the
domain, testing different algorithms and showing the results.