# Powershap: A Power-full Shapley Feature Selection Method

arXiv: https://arxiv.org/abs/2206.08394 · HTML: https://mlsanity.com/paper/2206.08394/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)

## Built on by (most cited first, 7 of 7)

- [A feature selection method based on Shapley values robust to concept shift in regression](https://mlsanity.com/paper/2304.14774/) (2023)
- [LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method](https://mlsanity.com/paper/2401.12683/) (2024)
- [pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning](https://mlsanity.com/paper/2309.14404/) (2023)
- [Shap-Select: Lightweight Feature Selection Using SHAP Values and Regression](https://mlsanity.com/paper/2410.06815/) (2024)
- [Statistical Inference and Learning for Shapley Additive Explanations (SHAP)](https://mlsanity.com/paper/2602.10532/) (2026)
- [KnobTree: Intelligent Database Parameter Configuration via Explainable Reinforcement Learning](https://mlsanity.com/paper/2406.15073/) (2024)
- [Which Hyperparameters Matter? A Game-Theoretic Framework for Interpretable Hyperparameter Sensitivity Analysis](https://mlsanity.com/paper/2607.15884/) (2026)

## Abstract (arXiv)

Feature selection is a crucial step in developing robust and powerful machine learning models. Feature selection techniques can be divided into two categories: filter and wrapper methods. While wrapper methods commonly result in strong predictive performances, they suffer from a large computational complexity and therefore take a significant amount of time to complete, especially when dealing with high-dimensional feature sets. Alternatively, filter methods are considerably faster, but suffer from several other disadvantages, such as (i) requiring a threshold value, (ii) not taking into account intercorrelation between features, and (iii) ignoring feature interactions with the model. To this end, we present powershap, a novel wrapper feature selection method, which leverages statistical hypothesis testing and power calculations in combination with Shapley values for quick and intuitive feature selection. Powershap is built on the core assumption that an informative feature will have a larger impact on the prediction compared to a known random feature. Benchmarks and simulations show that powershap outperforms other filter methods with predictive performances on par with wrapper methods while being significantly faster, often even reaching half or a third of the execution time. As such, powershap provides a competitive and quick algorithm that can be used by various models in different domains. Furthermore, powershap is implemented as a plug-and-play and open-source sklearn component, enabling easy integration in conventional data science pipelines. User experience is even further enhanced by also providing an automatic mode that automatically tunes the hyper-parameters of the powershap algorithm, allowing to use the algorithm without any configuration needed.