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.

本论文提出了一种基于统计假设检验和 Shapley 值的新型包装特征选择方法 powershap，通过对预测结果的影响进行快速且直观的特征选择。该方法速度更快且价格更具竞争力，并且由于实现为 sklearn 组件，可以轻松集成到数据科学流程中。