klotz: feature selection*

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  1. History-based Feature Selection (HBFS) is a feature selection tool that aims to identify an optimal subset of features for prediction problems. It is designed to work similarly to wrapper methods and genetic methods, focusing on selecting feature subsets that yield the highest performance for a given dataset and target. HBFS differs from filter methods, which evaluate and rank individual features based on their predictive power. Instead, HBFS evaluates combinations of features over multiple iterations, using a Random Forest regressor to estimate performance and iteratively refining feature sets. This tool supports binary and multiclass classification, as well as regression, and allows for balancing the trade-off between maximizing accuracy and minimizing the number of features through parameters such as maximum features and penalties. Examples provided demonstrate the use of HBFS with various models and metrics, showcasing its ability to improve model performance by identifying optimal feature subsets.

  2. This article provides an overview of feature selection in machine learning, detailing methods to maximize model accuracy, minimize computational costs, and introduce a novel method called History-based Feature Selection (HBFS).

  3. Generating counterfactual explanations got a lot easier with CFNOW, but what are counterfactual explanations, and how can I use them?

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