It’s what we do


Automunge is an open source python library that has formalized and automated the data preparations for tabular learning in between the workflow boundaries of received “tidy data” (one column per feature and one row per sample) and returned dataframes suitable for the direct application of machine learning. Under automation numeric…

Keep Calm and Mind the Details

Appendix A — Table of Contents

  • Appendix B — Train and Test Data
  • Appendix C — Noise Options
  • Appendix D — Automunge Demonstration
  • Appendix E — Distribution Scaling
  • Appendix F — Sampling Parameters
  • Appendix G — Causal Inference
  • Appendix H — Noise Injection Tutorial
  • H.1 — DP transformation categories
  • H.2 — Parameter Assignment
  • H.3 — Numeric…

Deep Regularization


The volume of the distribution of possible weight configurations 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. This paper introduces geometric regularization and explores potential applicability to several…

With Automunge


Injecting gaussian noise into training features is well known to have regularization properties. This paper considers noise injections to numeric or categoric tabular features as passed to inference, which translates inference to a non-deterministic outcome and may have relevance to fairness considerations, adversarial example protection, or other use cases benefiting…

The fine print


As followers of this blog may be aware, the author has recently been offering some hypotheses regarding potential benefits of noise injections in the context of tabular learning applications. It is probably worth reiterating that several aspects of these suggestions are merely that, hypotheses. We have been building out features…

Nicholas Teague

Writing for fun and because it helps me organize my thoughts. I also write software to prepare data for machine learning at

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