Numeric Encoding Options with Automunge
Nicholas J. Teague

TL;DR
This paper explores the benefits of extended numeric feature encodings in deep learning for tabular data, using Automunge's transformation options to improve model performance through data augmentation.
Contribution
It introduces a comprehensive survey of numeric transformation options in Automunge and demonstrates a novel generalized noise injection method for data augmentation.
Findings
Extended encodings can enhance deep learning on tabular data.
Noise injection improves model performance with limited training data.
Family tree transformations enable flexible feature engineering.
Abstract
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 networks. This paper will offer arguments for potential benefits of extended encodings of numeric streams in deep learning by way of a survey of options for numeric transformations as available in the Automunge open source python library platform for tabular data pipelines, where transformations may be applied to distinct columns in "family tree" sets with generations and branches of derivations. Automunge transformation options include normalization, binning, noise injection, derivatives, and more. The aggregation of these methods into family tree sets of transformations are demonstrated for use to present numeric features to machine learning in multiple configurations of varying information content, as may…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning and Data Classification · Explainable Artificial Intelligence (XAI) · Reservoir Engineering and Simulation Methods
