XAI
DALEX
XAI | DALEX | |
---|---|---|
1 | 2 | |
18 | 1,326 | |
- | 0.8% | |
0.0 | 5.9 | |
almost 2 years ago | 2 days ago | |
Python | Python | |
- | GNU General Public License v3.0 only |
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XAI
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My failure of using Python DEAP package.
An example of custom functions can be seen at: https://github.com/ben-ix/XAI/blob/master/src/base.py
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).
TalkToModel - TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
captum - Model interpretability and understanding for PyTorch
pytorch-lattice - A PyTorch implementation of constrained optimization and modeling techniques
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.