pat-alt / optimalsubsampling

This project investigates if and how systematic subsampling can be applied to imbalanced learning.

README

optimalSubsampling

This project investigates if and how systematic subsampling can be applied to imbalanced learning. All details can be found in this Jupyter notebook - good if you want a condensed, interactive version and like working with Jupyter notebooks. For a better reading experience I would recommend using the more detailed HTML. A quick overview is provided below.

Overview

The case for subsampling involves n >  > p, so very large values of n. In such cases we may be interested in estimating model coefficients β̂_m instead of β̂_n where p ≤ m <  < n with m freely chosen by us. In practice we may want to do this to avoid high computational costs associated with large n as discussed above. The basic algorithm for estimating β̂_m is simple:

1. Subsample with replacement from the data with some sampling probability {π_i}.
2. Estimate least-squares estimator β̂_m using the subsample.

Here we look at a few of the different subsampling methods investigated and proposed in Zhu et al, 2015, which differ primarily in their choice of subsampling probabilities {π_i}. The baseline results from Zhu et al, 2015, are replicated here and consistent with the authors’ findings: systematic subsampling can greatly improve model performance.