1. Introduction

PyDePr (“pie-deeper”) is a toolkit designed to facilitate the preprocessing and validation of machine learning models. What does that mean? Machine learning models are often specialized, requiring years of experience to correctly configure. The hardest step is often preprocessing the dataset to be analyzed. An old joke in machine learning is that 90% of the work is cleaning the dataset, while only 10% is actual data analysis.

Keeping that in mind, PyDePr is meant to ease the initial step of model-building by automatically preprocessing some very specific types of machine learning models. PyDePr is not meant to build machine learning models for you (there are plenty of Python libraries for that purpose), and it is not meant to be some sort of catch-all solution for all types of machine learning models. However, support for additional model types will be added as PyDePr is developed.

Package Organization

PyDePr is organized into several namespaces, including:

• regression
• inference
• waveform

The regression namespace handles all preprocessing and validation for regression models, including a visualization of results. The inference namespace is meant for Bayesian inference, where accumulated evidence points to a conclusion.