# 讀書會時程規劃
* 閱讀書籍 - Interpretable Machine Learning
A Guide for Making Black Box Models Explainable.
- 兩篇論文：
https://arxiv.org/pdf/2001.02478.pdf
https://arxiv.org/pdf/1909.03012.pdf

* 書籍網址 https://christophm.github.io/interpretable-ml-book/
* 倚任副理建議直接先閱讀第五章和第六章
* 讀書計畫初稿如下表，從下週二開始分享，之後視情況可機動調整

| Chapter 5 Model-Agnostic Methods|負責組別|日期 |
| -------- | -------- | -------- |
|5.1 Partial Dependence Plot (PDP)|G1|3/3|
|5.2 Individual Conditional Expectation (ICE)|G2|3/10|
|5.3 Accumulated Local Effects (ALE) Plot|G3|3/17|
|展示作業成果(PDP、ICE、ALE)|ALL|3/24|
|5.4 Feature Interaction|G1|3/31|
|5.5 Permutation Feature Importance|G2|4/14|
|5.6 Global Surrogate|G3|4/21|
|5.7 Local Surrogate (LIME)|G1|4/28|
|5.8 Scoped Rules (Anchors)|G2|4/28|
|5.9 Shapley Values|G3|5/5|
|5.10 SHAP (SHapley Additive exPlanations)|G1|5/12|

| Chapter 6 Example-Based Explanations|負責組別|日期 |
| -------- | -------- | -------- |
|6.1 Counterfactual Explanations|G2|5/19 |
|6.2 Adversarial Examples |G3|5/26 |
|6.3 Prototypes and Criticisms |G3 |6/2 |
|6.4 Influential Instances | ALL|6/9 |

###### tags: `其他`