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Model Agnostic Interpretation Library

Project description

Skater

Skater is a python package for model agnostic interpretation of predictive models. With Skater, you can unpack the internal mechanics of arbitrary models; as long as you can obtain inputs, and use a function to obtain outputs, you can use Skater to learn about the models internal decision policies.

The package was originally developed by Aaron Kramer, Pramit Choudhary and internal DataScience Team at DataScience.com to help enable practitioners explain and interpret predictive “black boxes” in a human interpretable way.

📖 Documentation

Overview

Introduction to the Skater library

Installing

How to install the Skater library

Tutorial

Steps to use Skater effectively.

API Reference

The detailed reference for Skater’s API.

Contributing

Guide to contributing to the Skater project.

💬 Feedback/Questions

Feature Requests/Bugs

GitHub issue tracker

Usage questions

Gitter chat

General discussion

Gitter chat

Install Skater

Dependencies

Skater relies on numpy, pandas, scikit-learn, and the DataScience.com fork of the LIME package. Plotting functionality requires matplotlib, though it is not required to install the package. Currently we only distribute to pypi, though adding a conda distribution is on the roadmap.

pip

When using pip, to ensure your system is not modified by an installation, it is recommended that you use a virtual environment (virtualenv, conda environment).

pip install -U Skater

Project details


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Source Distribution

skater-1.0.1.tar.gz (36.9 kB view hashes)

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