Metadata-Version: 1.1
Name: ibreakdown
Version: 0.0.1a3
Summary: ibreakdown - model agnostic explanations with interactions
Home-page: https://github.com/jettify/ibreakdown
Author: Nikolay Novik
Author-email: nickolainovik@gmail.com
License: Apache 2
Download-URL: https://pypi.python.org/pypi/ibreakdown
Description: ibreakdown
        ==========
        .. image:: https://travis-ci.com/jettify/ibreakdown.svg?branch=master
            :target: https://travis-ci.com/jettify/ibreakdown
        .. image:: https://codecov.io/gh/jettify/ibreakdown/branch/master/graph/badge.svg
            :target: https://codecov.io/gh/jettify/ibreakdown
        .. image:: https://img.shields.io/pypi/pyversions/ibreakdown.svg
            :target: https://pypi.org/project/ibreakdown
        .. image:: https://img.shields.io/pypi/v/ibreakdown.svg
            :target: https://pypi.python.org/pypi/ibreakdown
        
        
        **ibreakdown** is model agnostic predictions explainer with interactions support,
        library can show contribution of each feature in your prediction value.
        
        **SHAP** or **LIME** consider only local additive feature attributions, when
        **ibreakdown** also evaluates local feature interactions.
        
        Algorithm
        =========
        
        Algorithm is based on ideas describe in paper *"iBreakDown: Uncertainty of Model
        Explanations for Non-additive Predictive Models"* https://arxiv.org/abs/1903.11420 and
        reference implementation in **R** (iBreakDown_)
        
        Intuition behind algorithm is following:
        
          ::
        
           The algorithm works in a similar spirit as SHAP or Break Down but is not
           restricted to additive effects. The intuition is the following:
        
           1. Calculate a single-step additive contribution for each feature.
           2. Calculate a single-step contribution for every pair of features. Subtract additive contribution to assess the interaction specific contribution.
           3. Order interaction effects and additive effects in a list that is used to determine sequential contributions.
        
           This simple intuition may be generalized into higher order interactions.
        
        In depth explanation can be found in algorithm authors free book:
        *Predictive Models: Explore, Explain, and Debug* https://pbiecek.github.io/PM_VEE/iBreakDown.html
        
        Features
        ========
        * Supports predictions explanations for classification and regression
        * Easy to use API.
        * Works with ``pandas`` and ``numpy``
        * Support interactions between features
        
        
        Installation
        ------------
        Installation process is simple, just::
        
            $ pip install ibreakdown
        
        
        Requirements
        ------------
        
        * Python_ 3.6+
        * numpy_
        
        .. _Python: https://www.python.org
        .. _numpy: http://www.numpy.org/
        .. _iBreakDown: https://github.com/ModelOriented/iBreakDown
        .. _Shapley: https://en.wikipedia.org/wiki/Shapley_value
        
        CHANGES
        =======
Keywords: ibreakdown,model explanation,xai
Platform: POSIX
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: POSIX
Classifier: Development Status :: 2 - Pre-Alpha
