https://github.com/rbhatia46/interpretable-machine-learning

In this project, I worked on Interpreting the results generated on a Financial dataset for a Binary Classification problem. Mainly, there are 2 frameworks used in this project, i.e., ELI5 and LIME.
https://github.com/rbhatia46/interpretable-machine-learning

Last synced: 7 months ago
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In this project, I worked on Interpreting the results generated on a Financial dataset for a Binary Classification problem. Mainly, there are 2 frameworks used in this project, i.e., ELI5 and LIME.

• Host: GitHub
• URL: https://github.com/rbhatia46/interpretable-machine-learning
• Owner: rbhatia46
• Created: 2019-05-21T18:22:08.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-05-21T18:39:15.000Z (over 6 years ago)
• Last Synced: 2025-01-24T18:37:04.967Z (9 months ago)
• Language: Jupyter Notebook
• Size: 1.33 MB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
**Important Note: The notebook is graphic intensive and therefore GitHub doesn't show all the proper visualizations necessary for interpreting the models used, therefore, please use nbviewer by clicking on the minus sign as indicated by GitHub as shown in the image below**

![](https://i.ibb.co/d60rq1T/Screenshot-from-2019-05-22-00-06-29.png)

# Interpretable-Machine-Learning

In this project, I worked on Interpreting the results generated on a Financial dataset for a Binary Classification problem. Mainly, there are 2 frameworks used in this project, i.e., [eli5](https://github.com/TeamHG-Memex/eli5) and [LIME](https://github.com/marcotcr/lime).

* ELI5 works well on simple models(white-box models).

* LIME(Model Agnostic) on the other hand can be used to interpret any type of model of any complexity(black-box models).