OmniXAI
DALEX
| OmniXAI | DALEX | |
|---|---|---|
| 1 | 2 | |
| 812 | 1,326 | |
| 2.5% | 0.8% | |
| 4.6 | 5.9 | |
| 15 days ago | 4 days ago | |
| Jupyter Notebook | Python | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 only |
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OmniXAI
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Salesforce AI Open-Sources ‘OmniXAI’: A Python-based Machine Learning Library That Provides One-Stop Explainable AI (XAI) Solution To analyze, Debug, And Interprets AI Models
Continue reading | Checkout the paper, article, github, dashboard
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?
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
interpret - Fit interpretable models. Explain blackbox machine learning.
captum - Model interpretability and understanding for PyTorch
eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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.
SegGradCAM - SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping
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.