1 citations · 1 across the 4 of their papers we have counts for
4 papers
Feature Encodings for Gradient Boosting with Automunge
Nicholas J. Teague
Automunge is a tabular preprocessing library that encodes dataframes for supervised learning. When selecting a default feature encoding strategy for gradient boosted learning, one…
Parsed Categoric Encodings with Automunge
Nicholas J. Teague
The Automunge open source python library platform for tabular data pre-processing automates feature engineering data transformations of numerical encoding and missing data infill t…
Numeric Encoding Options with Automunge
Nicholas J. Teague
Mainstream practice in machine learning with tabular data may take for granted that any feature engineering beyond scaling for numeric sets is superfluous in context of deep neural…
Missing Data Infill with Automunge
Nicholas J. Teague
Missing data is a fundamental obstacle in the practice of data science. This paper surveys a few conventions for imputation as available in the Automunge open source python library…