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Automunge is a tabular preprocessing library that encodes dataframes forsupervised learning. When selecting a default feature encoding strategy forgradient boosted learning, one may consider metrics of training duration andachieved predictive performance associated with the feature representations.Automunge offers a default of binarization for categoric features and z-scorenormalization for numeric. The presented study sought to validate thosedefaults by way of benchmarking on a series of diverse data sets by encodingvariations with tuned gradient boosted learning. We found that on average ourchosen defaults were top performers both from a tuning duration and a modelperformance standpoint. Another key finding was that one hot encoding did notperform in a manner consistent with suitability to serve as a categoric defaultin comparison to categoric binarization. We present here these and furtherbenchmarks.

Feature Encodings for Gradient Boosting with Automunge