# Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence

## TL;DR

This study evaluates the predictive power of 171 active region properties for solar flare forecasting using machine learning, revealing redundancy among features and emphasizing the probabilistic nature of flare prediction.

## Contribution

It introduces a comprehensive analysis of a large set of physics-based properties and demonstrates the importance of objective training and feature redundancy in flare prediction.

## Key findings

- Most properties are redundant for flare prediction.
- Objective training significantly improves model performance.
- Flare prediction remains fundamentally probabilistic.

## Abstract

Solar flares originate from magnetically active regions but not all solar active regions give rise to a flare. Therefore, the challenge of solar flare prediction benefits by an intelligent computational analysis of physics-based properties extracted from active region observables, most commonly line-of-sight or vector magnetograms of the active-region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive active region properties, mainly inferred by the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) in the course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine learning methods that allow feature ranking as a function of predictive capability, we show that: i) an objective training and testing process is paramount for the performance of every supervised machine learning method; ii) most properties include overlapping information and are therefore highly redundant for flare prediction; iii) solar flare prediction is still - and will likely remain - a predominantly probabilistic challenge.