Résultats du processus de synthèse de l'article arXiv : 2209.12309v1

Résumé : Selecting a default feature encoding strategy for gradient boosted learning may consider metrics of training duration and achieved predictive performance associated with the feature representations. The Automunge library for dataframe preprocessing offers a default of binarization for categoric features and z-score normalization for numeric. The presented study sought to validate those defaults by way of benchmarking on a series of diverse data sets by encoding variations with tuned gradient boosted learning. We found that on average our chosen defaults were top performers both from a tuning duration and a model performance standpoint. Another key finding was that one hot encoding did not perform in a manner consistent with suitability to serve as a categoric default in comparison to categoric binarization. We present here these and further benchmarks.