xplainable
DALEX
xplainable | DALEX | |
---|---|---|
2 | 2 | |
52 | 1,326 | |
- | 0.8% | |
9.0 | 5.9 | |
14 days ago | 3 days ago | |
Python | Python | |
GNU Affero General Public License v3.0 | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
xplainable
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Explainable (Structured) Machine Learning Algorithm
Just for some respite from the discussion of our soon-to-be AI overlords (LLMs), I'm one of the contributors to an open-source Python package, Xplainable (https://github.com/xplainable/xplainable). Xplainable is a novel (structured) machine learning algorithm that's inherently explainable, as opposed to being a post-hoc explainer (like SHAP or Lime).
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Tools for documenting OS Python Package
I'm looking at migrating the docs for our open-source Python package https://github.com/xplainable/xplainable from sphinx to something else. I was initially looking at either docosaurus or Mintlify. Mintlify looks substantially easier to setup but I'm questioning the extensibility (also the cost).
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?
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
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
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.
EthicML - Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency
alibi - Algorithms for explaining machine learning models