ML Interpratation, Comprehensibility & Causality
2018-04-17.
Category & Tags:
Machine Learning,
Interpretability,
Comprehensibility,
Understandability,
Explain
See also:
DL-theory > ## cnn visualization/comprehensibility
- 可解释的机器学习 (What)
- 可解释性的重要性 (Why)
- 具体如何解释 (How)
- Insights which can be extracted from the models
- Permutation Importance
- Partial Dependency Plots
- SHAP Values
- SHAP Values in Advance
LIME (万金油), Tree interpreter, etc.
凭什么相信你，我的CNN模型？关于CNN模型可解释性的思考 inc.:CAM, Grad-CAM, Lime.
Book by Christoph Molnar: Interpretable Machine Learning – A Guide for Making Black Box Models Explainable (GitHub), (CN)
All-in-one: SHAP.
ref:
https://medium.com/@Zelros/a-brief-history-of-machine-learning-models-explainability-f1c3301be9dc
Causality / Causal Inference #
- Jonas Peters, Dominik Janzing and Bernhard Schölkopf 2017 - Elements of Causal Inference ;
- Bernhard Schölkopf 2019 - Causality for Machine Learning,
via 贝叶斯网络之父Judea Pearl力荐、LeCun点赞，这篇长论文全面解读机器学习中的因果关系 机器学习研究会订阅号 - 图灵奖得主Judea Pearl ：从“贝叶斯网络之父”到“AI社区的叛徒”
Ref:
en towardsdatascience.com
cn weixin
SHAP for ML.
