# A hybrid supervised/unsupervised machine learning approach to solar flare prediction

## TL;DR

This paper presents a hybrid machine learning approach combining supervised regularization and unsupervised clustering to improve solar flare prediction, validated with NOAA SWPC data.

## Contribution

It introduces a novel hybrid method that integrates supervised and unsupervised techniques for more accurate solar flare prediction.

## Key findings

- Effective feature importance identification through regularization.
- Successful binary flare/no-flare classification via clustering.
- Validated approach with NOAA SWPC data.

## Abstract

We introduce a hybrid approach to solar flare prediction, whereby a supervised regularization method is used to realize feature importance and an unsupervised clustering method is used to realize the binary flare/no-flare decision. The approach is validated against NOAA SWPC data.