Metadata-Version: 1.2
Name: bcselector
Version: 0.0.24
Summary: Python package to help you in variable selection.
Home-page: https://github.com/Kaketo/bcselector
Author: Tomasz Klonecki
Author-email: tomasz.klonecki@gmail.com
License: MIT
Description: ==========
        Bcselector
        ==========
        .. image:: https://raw.githubusercontent.com/Kaketo/bcselector/master/docs/img/logo_small.png
        
        .. image:: https://img.shields.io/badge/python-3.7-blue.svg
            :target: http://badge.fury.io/py/bcselector
        .. image:: https://badge.fury.io/py/bcselector.svg
            :target: https://badge.fury.io/py/bcselector
        .. image:: https://travis-ci.com/Kaketo/bcselector.svg?branch=master
            :target: https://travis-ci.com/Kaketo/bcselector
        .. image:: https://codecov.io/gh/Kaketo/bcselector/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/Kaketo/bcselector
        .. image:: https://img.shields.io/badge/License-MIT-yellow.svg
          :target: https://opensource.org/licenses/MIT
        
        * Documentation: https://kaketo.github.io/bcselector.
        * Repository: https://github.com/kaketo/bcselector.
        
        What is it?
        -----------
        Feature selection is a crucial problem in many machine learning tasks. Usually the considered
        variables are cheap to collect and store but in some situations the acquisition of feature values
        can be problematic. For example, when predicting the occurrence of the disease we may consider
        the results of some diagnostic tests which can be very expensive.
        The existing feature selection methods usually ignore costs associated with the considered
        features. The goal of cost- sensitive feature selection is to select a subset of features which allow
        to predict the target variable (e.g. occurrence of the diseases) successfully within the assumed
        budget.
        
        The main purpose of this package is to provide filter methods of feature selection based
        on information theory and to propose new variants of these methods considering feature costs.
        
        
        Installation
        ------------
        
        bcselector can be installed from [PyPI](https://pypi.org/project/bcselector): ::
        
            pip install bcselector
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
