awesome-shapley-value
DALEX
awesome-shapley-value | DALEX | |
---|---|---|
1 | 2 | |
133 | 1,326 | |
4.5% | 0.8% | |
3.2 | 5.9 | |
almost 2 years ago | 4 days ago | |
Python | ||
Apache License 2.0 | GNU General Public License v3.0 only |
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awesome-shapley-value
DALEX
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Twitter set to accept ‘best and final offer’ of Elon Musk
Which he will not do, because: a) He can't, it's a black box algorithm. It actually is open source already, but that doesn't mean much as it's useless without Twitter's data https://github.com/ModelOriented/DALEX b) He won't release data that shows the algorithm is racist and amplifies conservative and extremist content. He won't remove such functions because it will cost him billions.
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[D] What are your favorite Random Forest implementations that support categoricals
There are a couple of ways to use Shapley values for explanations in R. One way is to use DALEX, which also contains a lot of other methods besides SHAP. Another one is iml. I am sure there are several other implementations of SHAP as well.
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.
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
AIX360 - Interpretability and explainability of data and machine learning models
captum - Model interpretability and understanding for PyTorch
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]
interpret - Fit interpretable models. Explain blackbox machine learning.
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.