https://github.com/pat-alt/optimalsubsampling
This project investigates if and how systematic subsampling can be applied to imbalanced learning.
https://github.com/pat-alt/optimalsubsampling
bias-variance subsampling
Last synced: 4 months ago
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This project investigates if and how systematic subsampling can be applied to imbalanced learning.
- Host: GitHub
- URL: https://github.com/pat-alt/optimalsubsampling
- Owner: pat-alt
- Created: 2020-11-15T13:05:55.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-01-01T15:26:11.000Z (about 5 years ago)
- Last Synced: 2025-10-10T21:41:38.953Z (5 months ago)
- Topics: bias-variance, subsampling
- Language: Jupyter Notebook
- Homepage:
- Size: 14.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
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README
---
title: "README"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# optimalSubsampling
This project investigates if and how systematic subsampling can be applied to imbalanced learning.
All details can be found in this [Jupyter notebook](notebook.ipynb) - 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](model_selection.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 $\hat\beta_m$ instead of $\hat\beta_n$ where $p\le m<