DALEX
shapley
| DALEX | shapley | |
|---|---|---|
| 2 | 7 | |
| 1,323 | 210 | |
| 0.6% | - | |
| 5.5 | 2.7 | |
| 2 months ago | 10 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
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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.
shapley
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AstraZeneca Researchers Explain the Concept and Applications of the Shapley Value in Machine Learning
Code for https://arxiv.org/abs/2202.05594 found: https://github.com/benedekrozemberczki/shapley
- Calculating and approximating the Shapley value in voting games
- Show HN: Pruning Machine Learning Models with the Shapley Value
- Show HN: Shapley: Explaining Machine Learning Ensembles
- Shapley - a Python library for solving weighted voting games.
- Show HN: Shapley – a Python library for scoring ML models in an ensemble
What are some alternatives?
captum - Model interpretability and understanding for PyTorch
autogluon - Fast and Accurate ML in 3 Lines of Code
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
sagemaker-explaining-credit-decisions - Amazon SageMaker Solution for explaining credit decisions.
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.
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
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.
AIX360 - Interpretability and explainability of data and machine learning models
interpret - Fit interpretable models. Explain blackbox machine learning.
csle - A research platform to develop automated security policies using quantitative methods, e.g., optimal control, computational game theory, reinforcement learning, optimization, evolutionary methods, and causal inference.