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.

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.