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.

The volume of the distribution of weight sets associated with a loss value may be the source of implicit regularization from overparameterization due to the phenomenon of contracting volume with increasing dimensions for geometric figures demonstrated by hyperspheres. We introduce the geometric regularization conjecture and extract to an explanation for the double descent phenomenon by considering a similar property resulting from shrinking intrinsic dimensionality of the distribution of potential weight set updates available along training path, where if that distribution retracts across a volume verses dimensionality curve peak when approaching the global minima we could expect geometric regularization to re-emerge. We illustrate how data fidelity representational complexity may influence model capacity double descent interpolation thresholds. The existence of epoch and model capacity double descent curves originating from different geometric forms may imply universality of closed n-manifolds having dimensionally adjusted n-sphere volumetric correspondence.

US Transportation Secretary Anthony Foxx delivers an announcement in Washington, DC, in 2014. Automakers and ride-hail companies are racing to put self-driving cars on the road. In a few weeks, Uber passengers in Pittsburgh will be able to hail self-driving Volvos. Last month, Tesla announced its hopes to build an autonomous ride-hailing fleet. And this month, Ford said it plans to mass-produce autonomous vehicles by 2021.