Metadata-Version: 2.1
Name: vimpy
Version: 0.0.3
Summary: vimpy: nonparametric variable importance assessment in python
Home-page: https://github.com/bdwilliamson/vimpy
Author: Brian Williamson
Author-email: brianw26@uw.edu
License: UNKNOWN
Description: # vimpy: nonparametric variable importance assessment in python
        
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        
        
        **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
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
