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- W4297435210 abstract "Automunge is a tabular preprocessing library that encodes dataframes for supervised learning. When selecting a default feature encoding strategy for gradient boosted learning, one may consider metrics of training duration and achieved predictive performance associated with the feature representations. Automunge 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." @default.
- W4297435210 created "2022-09-28" @default.
- W4297435210 creator A5005970308 @default.
- W4297435210 date "2022-09-25" @default.
- W4297435210 modified "2023-10-18" @default.
- W4297435210 title "Feature Encodings for Gradient Boosting with Automunge" @default.
- W4297435210 doi "https://doi.org/10.48550/arxiv.2209.12309" @default.
- W4297435210 hasPublicationYear "2022" @default.
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