TL;DR
Feature engineering is the clever engineering of data hereby exploiting the intrinsic bias of the machine learning technique to improve the performance of any machine learning model, ideally both in terms of accuracy and interpretability at the same time.
Often, the initial data sets for machine learning contain an excessive number of features, among which there may be highly informative and insignificant, correlating or even noise variables. Solving the feature selection problem not only optimizes machine learning processes, but also opens up new opportunities for impl...
Optimization is a critical research area with increasing relevance to machine learning. For a range of machine learning tasks—such as hyperparameter tuning, knowledge transfer, and feature selection—optimization techniques are essential. Hyperparameter tuning, for instance, relies on optimization to identify the ideal...
AI is a stream of science related to intelligent machine learning, mainly intelligent computer programs, which provides results in a similar way to the human attention process[1] . This process generally comprises obtaining data, developing efficient systems for the uses of obtained data, illustrating definite or appro...
The growing importance of data-driven decision making in healthcare, and also other domains like intelligent analytics, has motivated the need for models that are able to achieve high predictive performance while still being interpretable. However, prior works often focused on data mining that contribute transparent ru...
This paper combines symbolic regression with LLMs for feature engineering (SymboLLM-FE) to solve the dual challenges of poor interpretability and numerous iterations by employing a statistical prior-grounded LLM refinement mechanism and single-digit LLM calls.
This framework identifies where models systematically `confuse'alternatives, highlighting trade-offs between alternatives, and potentially guiding model specification, and potentially guiding model specification.
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