awesome-shapley-value
interpret
| awesome-shapley-value | interpret | |
|---|---|---|
| 1 | 6 | |
| 133 | 6,007 | |
| 4.5% | 0.6% | |
| 3.2 | 9.7 | |
| almost 2 years ago | 6 days ago | |
| C++ | ||
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-shapley-value
interpret
-
[D] Alternatives to the shap explainability package
Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods.
-
What Are the Most Important Statistical Ideas of the Past 50 Years?
You may also find Explainable Boosting Machines interesting: https://github.com/interpretml/interpret
They're a bit like a best of both worlds between linear models and random forests (generalized additive models fit with boosted decision trees)
Disclosure: I helped build this open source package
-
[N] Google confirms DeepMind Health Streams project has been killed off
Microsoft Explainable Boosting Machine (which is a Gaussian Additive Model and not a Gradient Boosted Trees 🙄 model) is a step in that direction https://github.com/interpretml/interpret
-
[Discussion] XGBoost is the way.
Also I'd recommend everyone who works with xgboost to give EBM's a try! They perform comparably (except in the case of extreme interactions) but are actually interpretable! https://github.com/interpretml/interpret/ Beside that they since on runtime they're practically a lookup table they're very quick (at the cost of longer training time).
-
[D] Generalized Additive Models… with trees?
Open source code by Microsoft: https://github.com/interpretml/interpret (called EBM in this implementation).
-
Machine Learning with Medical Data (unbalanced dataset)
If it's not an image, have a go at Microsoft's Explainable Boosting Maching) https://github.com/interpretml/interpret which is not a GBM but a GAM (Gradient Boosting Machine vs Gradient Additive Model). This will also give you explanation via SHAP or LIME values.
What are some alternatives?
responsible-ai-toolbox - Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
shap - A game theoretic approach to explain the output of any machine learning model.
AIX360 - Interpretability and explainability of data and machine learning models
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
alibi - Algorithms for explaining machine learning models
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
DALEX - moDel Agnostic Language for Exploration and eXplanation
medspacy - Library for clinical NLP with spaCy.
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest
DashBot-3.0 - Geometry Dash bot to play & finish levels - Now training much faster!