shap
skope-rules
| shap | skope-rules | |
|---|---|---|
| 38 | 3 | |
| 21,677 | 590 | |
| 1.1% | 0.5% | |
| 9.3 | 0.0 | |
| 5 days ago | 3 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | GNU General Public License v3.0 or later |
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shap
- Shap v0.45.0
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[D] Convert a ML model into a rule based system
something like GitHub - shap/shap: A game theoretic approach to explain the output of any machine learning model.?
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[P] tinyshap: A minimal implementation of the SHAP algorithm
A less than 100 lines of code implementation of KernelSHAP because I had a hard time understanding shap's code.
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What’s after model adequacy?
We use tools like SHAP to explain what the model is doing to stakeholders.
- Feature importance with feature engineering?
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Model interpretation with many features
https://github.com/slundberg/shap this or https://github.com/marcotcr/lime would be relevant to you, especially if you want to look at explaining a single prediction.
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SHAP Value Interpretation
See this closed topic for more detail: https://github.com/slundberg/shap/issues/29
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Christoph Molnar on SHAP Library
Dr. Molnar recently had a semi-viral post on LinkedIn and on Twitter, where he essentially highlights the booming popularity [and power] of using SHAP for explainable AI (which I agree with), but that it also comes with problems; i.e., the open source implementation has thousands of pull requests, bugs, and issues and yet there is no permanent or significant funding to go in and fix them.
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Random Forest Estimation Question
Option 4) create SHAP values https://github.com/slundberg/shap to better understand what the RF did.
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Model explainability
txtai pipelines are wrappers around Hugging Face pipelines with logic to easily integrate with txtai's workflow framework. Given that, we can use the SHAP library to explain predictions.
skope-rules
- [D] Convert a ML model into a rule based system
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Churn/Retention prediction without machine learning
You could look into using SkopeRules https://github.com/scikit-learn-contrib/skope-rules which trains a tree based model and picks out the best performing root-to-leaf paths as rules. It's ML under the hood but the final 'model' doesn't represent ML at all, since it would just be an if then statement.
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D Simple Questions Thread December 20 2020
Blackbox + SHAP/LIME usually too complex/overkill for cluster analysis. https://github.com/scikit-learn-contrib/skope-rules/blob/master/notebooks/demo_clustering.ipynb gives good results with very interpretable, diverse, decision rules.
What are some alternatives?
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
xgboost-survival-embeddings - Improving XGBoost survival analysis with embeddings and debiased estimators
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
captum - Model interpretability and understanding for PyTorch
lime - Lime: Explaining the predictions of any machine learning classifier
interpret - Fit interpretable models. Explain blackbox machine learning.
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
anchor - Code for "High-Precision Model-Agnostic Explanations" paper
lucid - A collection of infrastructure and tools for research in neural network interpretability.
articulated-animation - Code for Motion Representations for Articulated Animation paper
jellyfish - 🪼 a python library for doing approximate and phonetic matching of strings.
xbyak - a JIT assembler for x86(IA-32)/x64(AMD64, x86-64) MMX/SSE/SSE2/SSE3/SSSE3/SSE4/FPU/AVX/AVX2/AVX-512 by C++ header