Fico-Challenge

Explainable Machine Learning Challenge

This is the second place winning submission for the FICO Explainable Machine Learning Challenge created by Oscar Gomez and Steffen Holter. By combining instance level explanations and a general global model interpretation we have created an interactive application to visualize the logic behind each of the model’s decisions.

Individual Explanation

The local explanation visualizes each individual sample by showing the comparative values of each feature and their relative density distribution across the data set. Each explanation highlights the key features that the model used to make the decision. In addition, where applicable the solution suggests the necessary changes needed to reverse the decision.

Global Explanation

The global explanation acts as a way to aggregate the individual results across the whole data set. By either choosing to examine the key features or the necessary changes, the interactive hierarchy creates a comprehensive overview. Each click reveals an additional level of depth within the solution revealing more information.

Results Announcement

https://www.fico.com/en/newsroom/fico-announces-winners-of-inaugural-xml-challenge

Available Online at

http://www.ml-explainer.com/challenge_intro
http://nyuvis-web.poly.edu/projects/fico/intro

To run locally

Install python 3.6 or above
Pip install pandas, flask, numpy, scipy, sklearn
Open Webapp folder
Run hello.py
Open in localhost:5005

Suggested Specifications

Open on Mac or PC
Latest version of Google Chrome
Screen size of at least 13 inches