ferret
DALEX
| ferret | DALEX | |
|---|---|---|
| 2 | 2 | |
| 203 | 1,323 | |
| - | 0.6% | |
| 8.4 | 5.5 | |
| 3 days ago | 2 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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ferret
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[R] Introducing ferret, a new Python package to streamline interpretability on Transformers
Feel free to visit our repo and doc to find handy tutorials and our feature release plan.
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Researchers From Italy Introduce ‘ferret’: A Novel Python Library for Benchmarking Explainers on Transformers
Continue reading | Check out the paper and github link.
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?
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
alibi - Algorithms for explaining machine learning models
captum - Model interpretability and understanding for PyTorch
FastTreeSHAP - Fast SHAP value computation for interpreting tree-based models
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
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.
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.
interpret - Fit interpretable models. Explain blackbox machine learning.
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
sagemaker-explaining-credit-decisions - Amazon SageMaker Solution for explaining credit decisions.