Package to understand ML Models
Project description
============
ML Insights
============
Package to understand ML Models
Installation:
-------------
.. code-block:: bash
$ pip install ml_insights
Usage:
------
.. code-block:: python
>>> import ml_insights as mli
>>> xray = mli.ModelXRay(model, data)
Examples:
---------
`Notebook Examples and Useage <examples/>`_
Documentation:
--------------
https://ml-insights.readthedocs.io.
License:
--------
Free software: `MIT license <LICENSE>`_
Developed By:
------------
* Brian Lucena
* Ramesh Sampath
References
--------
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin. 2014. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation. Journal of Computational and Graphical Statistics (March 2014)
=======
History
=======
0.0.1 (2016-11-01)
------------------
* First release on PyPI.
0.0.2 (2016-11-02)
------------------
* Added Path between Points to ModelXRay.
ML Insights
============
Package to understand ML Models
Installation:
-------------
.. code-block:: bash
$ pip install ml_insights
Usage:
------
.. code-block:: python
>>> import ml_insights as mli
>>> xray = mli.ModelXRay(model, data)
Examples:
---------
`Notebook Examples and Useage <examples/>`_
Documentation:
--------------
https://ml-insights.readthedocs.io.
License:
--------
Free software: `MIT license <LICENSE>`_
Developed By:
------------
* Brian Lucena
* Ramesh Sampath
References
--------
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin. 2014. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation. Journal of Computational and Graphical Statistics (March 2014)
=======
History
=======
0.0.1 (2016-11-01)
------------------
* First release on PyPI.
0.0.2 (2016-11-02)
------------------
* Added Path between Points to ModelXRay.
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