# Model Explanation
## Overview
- https://speakerdeck.com/skydome20/xai-explainable-ai-introduction-and-dissection

## Note
- https://medium.com/fiddlerlabs/case-study-explaining-credit-modeling-predictions-with-shap-2a7b3f86ec12
- ***XXX*** helps explain the features most important to model predictions, but there is still a lot of nuance to understanding and using it correctly.

## SHAP
- https://github.com/slundberg/shap
- https://medium.com/@bhattacharyya.shilpi.sbu/explaining-black-box-models-ensemble-and-deep-learning-using-lime-and-shap-53c59d9f09b3
- https://medium.com/@gabrieltseng/interpreting-complex-models-with-shap-values-1c187db6ec83
- https://towardsdatascience.com/shap-explained-the-way-i-wish-someone-explained-it-to-me-ab81cc69ef30

## LIME
- https://medium.com/@bhattacharyya.shilpi.sbu/explaining-black-box-models-ensemble-and-deep-learning-using-lime-and-shap-53c59d9f09b3

## Visualization
#### MISC
A Visual History of Interpretation for Image Recognition
https://thegradient.pub/a-visual-history-of-interpretation-for-image-recognition/?utm_campaign=Akira%27s%20ML%20news&utm_medium=email&utm_source=Revue%20newsletter