vimpy 0.0.3

vimpy: nonparametric variable importance assessment in python

vimpy: nonparametric variable importance assessment in python

Author: Brian Williamson

Introduction

In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining an outcome; this is often called variable importance. It is useful to consider variable importance as a function of the unknown, underlying data-generating mechanism rather than the specific predictive algorithm used to fit the data. This package provides functions that, given fitted values from predictive algorithms, compute nonparametric estimates of deviance- and variance-based variable importance, along with asymptotically valid confidence intervals for the true importance.

Installation

You may install a stable release of vimpy using conda by

You may install the current dev releast of vimpy by downloading this repository directly.

Example