WhiteBox-Part2

The White Box Project is a project that introduces many ways to solve the part of the black box of machine learning. This project is based on Interpretable Machine Learning by Christoph Molnar [1]. I recommend you to read the book first and practice this project. If you are R user, you can see R code used in examples here.

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Purpose

The goal is to analysis various data into black box models and to build a pipeline of analysis reports using interpretable methods.

Requirements

numpy == 1.17.3
scikit-learn == 0.21.2
xgboost == 0.90
tensorflow == 1.14.0

Dataset

1. Titanic: Machine Learning from Disaster (Classification) [2]
2. Cervical Cancer (Classification) [3]
3. House Prices: Advanced Regression Techniques (Regression) [4]
4. Bike Sharing (Regression) [5]
5. Youtube Spam (Classification & NLP) [6]

Black Box Models

The parameters used to learn the model can be found here

1. Random Forest (RF)
2. XGboost (XGB)
3. LigthGBM (LGB)
4. Deep Neural Network (DNN)

Interpretable Methods

Model-specific methods [English|Korean]

• Linear Regression [English|Korean]
• Logistic Regression [English|Korean]
• GLM, GAM and more [English|Korean]
• Decision Tree [English|Korean]
• Decision Rules [English|Korean]
• RuleFit [English|Korean]
• Other Interpretable Models [English|Korean]

Model-agnostic methods [English|Korean]

• Partial Dependence Plot (PDP) [English|Korean]
• Individual Conditional Expectation (ICE) [English|Korean]
• Accumulated Local Effects (ALE) Plot [English|Korean]
• Feature Interaction [English|Korean]
• Permutation Feature Importance [English|Korean]
• Global Surrogate [English|Korean]
• Local Surrogate (LIME) [English|Korean]
• Scoped Rules (Anchors) [English|Korean]
• Shapley Values [English|Korean]
• SHAP (SHapley Additive exPlanations) [English|Korean]

Reference

[1] Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019. https://christophm.github.io/interpretable-ml-book/.

[2] Kaggle Competiton : Titanic: Machine Learning from Disaster

[3] Kelwin Fernandes, Jaime S. Cardoso, and Jessica Fernandes. 'Transfer Learning with Partial Observability Applied to Cervical Cancer Screening.' Iberian Conference on Pattern Recognition and Image Analysis. Springer International Publishing, 2017. [Link]

[4] Kaggle Competition : House Prices: Advanced Regression Techniques

[5] Fanaee-T, Hadi, and Gama, Joao, "Event labeling combining ensemble detectors and background knowledge", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007/s13748-013-0040-3. [Link]

[6] Alberto, T.C., Lochter J.V., Almeida, T.A. TubeSpam: Comment Spam Filtering on YouTube. Proceedings of the 14th IEEE International Conference on Machine Learning and Applications (ICMLA'15), 1-6, Miami, FL, USA, December, 2015. [Link]

[7] Lundberg, Scott M., and Su-In Lee. “A unified approach to interpreting model predictions.” Advances in Neural Information Processing Systems. 2017. (Korean Version)