Metadata-Version: 2.1
Name: PermutationImportance
Version: 1.0.3
Summary: Important variables determined through permutation selection
Home-page: https://github.com/gelijergensen/PermutationImportance
Author: G. Eli Jergensen
Author-email: gelijergensen@ou.edu
License: MIT
Description: # PermutationImportance
        
        Provides an efficient method to compute variable importance through the
        permutation of input variables. Uses multithreading and supports both Windows
        and Unix systems and Python 2 and 3.
        
        This repository provides the stand-alone functionality of a permutation-based
        method of computing variable importance in an arbitrary model. Importance for a
        given variable is computed in accordance with Lakshmanan et al. (2015)'s
        paper[1]. Variables which, when their values are permuted, cause the worst
        resulting score are considered most important. This implementation provides the
        functionality for an arbitrary method for computing the ``worst'' score and for
        using an arbitrary scoring metric. The most common case of this is to chose the
        variable which most negatively impacts the accuracy of the model.
        
        Functionality is provided not only for returning the most important variable
        (along with the raw scores for each variable) but also for returning the
        sequential importance of variables. To do this, the most important variable is
        determined and then it is left permuted while the next most important variable
        is determined. In the extreme case, this is continued until the sequential
        ordering of all variables is determined, but this can be terminated at an
        earlier level by choice.
        
        <sup>1</sup>Lakshmanan, V., C. Karstens, J. Krause, K. Elmore, A. Ryzhkov, and
        S. Berkseth, 2015: Which Polarimetric Variables Are Important for
        Weather/No-Weather Discrimination?. J. Atmos. Oceanic Technol., 32, 1209–1223,
        https://doi.org/10.1175/JTECH-D-13-00205.1
        
        ## Setup
        
        PermutationImportance is now available on pip, so you can simply install with
        `pip install PermutationImportance` and import the desired method with
        
        ```python
        from permutation_importance.variable_importance import permutation_selection_importance
        ```
        
Keywords: variable importance,model evaluation
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
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
Description-Content-Type: text/markdown
