Metadata-Version: 2.1
Name: PermutationImportance
Version: 1.2.1.7
Summary: Important variables determined through data-based variable importance methods
Home-page: https://github.com/gelijergensen/PermutationImportance
Author: G. Eli Jergensen
Author-email: gelijergensen@ou.edu
License: MIT
Description: # <span class="permutationimportancetitle">PermutationImportance</span>
        
        [![Build Status](https://travis-ci.com/gelijergensen/PermutationImportance.svg?branch=master)](https://travis-ci.com/gelijergensen/PermutationImportance)
        [![Documentation Status](https://readthedocs.org/projects/permutationimportance/badge/?version=latest)](https://permutationimportance.readthedocs.io/en/latest/?badge=latest)
        
        ![PermutationImportance Logo](https://github.com/gelijergensen/PermutationImportance/blob/master/docs/images/favicon.png)
        
        Welcome to the PermutationImportance library!
        
        PermutationImportance is a Python package for Python 2.7 and 3.6+ which provides
        several methods for computing data-based predictor importance. The methods
        implemented are model-agnostic and can be used for any machine learning model in
        many stages of development. The complete documentation can be found at our
        [Read The Docs](https://permutationimportance.readthedocs.io/en/latest/).
        
        ## Version History
        
        - 1.2.1.7: Fixed a bug where pandas dataframes were being unshuffled when 
          concatenated
        - 1.2.1.5: Added documentation and examples and ensured compatibility with
          Python 3.5+
        - 1.2.1.4: Original scores are now also bootstrapped to match the other results
        - 1.2.1.3: Corrected an issue with multithreading deadlock when returned scores
          were too large
        - 1.2.1.1: Provided object to assist in constructing scoring strategies
          - Also added two new strategies with bootstrapping support
        - 1.2.1.0: Metrics can now accept kwargs and support bootstrapping
        - 1.2.0.0: Added support for Sequential Selection and completely revised backend
          for proper abstraction and extension
          - Return object now keeps track of `(context, result)` pairs
          - `abstract_variable_importance` enables implementation of custom variable
            importance methods
          - Backend is now correctly multithreaded (when specified) and is
            OS-independent
        - 1.1.0.0: Revised return object of Permutation Importance to support easy
          retrieval of Breiman- and Lakshmanan-style importances
        - 1.0.0.0: Published with `pip` support!
        
Keywords: predictor importance,variable importance,model evaluation
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*
Description-Content-Type: text/markdown
