Package to understand ML Models
Project description
ML Insights
Package to understand Supervised ML Models. This package has been tested with Scikit-Learn and XGBoost library. It should work with any machine learning library that has a predict and predict_proba methods for regression and classification estimators.
The main entry point to this package is ModelXRay class. Instantiate it with the model and data. The data can be what the model was trained with, but inteded to be used for out of bag or test data to see how the model performs when one feature is changed, holding everything else constant.
We have not tested this for unsupervied models.
Python
Python 2.7 and 3.4+
Disclaimer
We have tested this tool to the best of my ability, but understand that it may have bugs. Use at your own risk!
Installation
$ pip install ml_insights
Usage
>>> import ml_insights as mli
>>> xray = mli.ModelXRay(model, data)
Source
Find the latest version on github: https://github.com/numeristical/introspective
Feel free to fork and contribute!
License
Free software: MIT 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.
0.0.4 (2016-11-11)
Added Path between Points to ModelXRay.
Project details
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