Nonparametric Feature Impact and Importance

2 code implementations • 8 Jun 2020 • Terence Parr, James D. Wilson, Jeff Hamrick

In this paper, we give mathematical definitions of feature impact and importance, derived from partial dependence curves, that operate directly on the data.

Feature Importance feature selection

Technical Report: Partial Dependence through Stratification

1 code implementation • 15 Jul 2019 • Terence Parr, James D. Wilson

Partial dependence curves (FPD) introduced by Friedman, are an important model interpretation tool, but are often not accessible to business analysts and scientists who typically lack the skills to choose, tune, and assess machine learning models.

The Matrix Calculus You Need For Deep Learning

5 code implementations • 5 Feb 2018 • Terence Parr, Jeremy Howard

This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks.

Deep Learning Math

Technical Report: Towards a Universal Code Formatter through Machine Learning

2 code implementations • 28 Jun 2016 • Terence Parr, Jurgin Vinju

There are many declarative frameworks that allow us to implement code formatters relatively easily for any specific language, but constructing them is cumbersome.

BIG-bench Machine Learning