Feature Encodings for Gradient Boosting with Automunge
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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.
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