DALEX
LIME
| DALEX | LIME | |
|---|---|---|
| 2 | 2 | |
| 1,323 | 14 | |
| 0.6% | - | |
| 5.5 | 0.0 | |
| 2 months ago | almost 3 years ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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.
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.
LIME
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[Explainable AI] Interpret complex neural network's decisions with simple linear regressions
I am very glad you liked it and thank you for your hint. In terms of citation, I never claimed the algorithm to be mine. But the implementation is 100% my work and the notebooks themselves are also only for educational purpose (university's course). On GitHub where the project's is hosted, I referenced the original author's work at the first place to ensure scientific integrity (https://github.com/longmakesstuff/LIME).
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Explainable AI: Interpreting black box models with simple linear regression
Basically, we desire to interpret how a black box model made its decision for a single sample. The code can be found at: https://github.com/longmakesstuff/LIME
What are some alternatives?
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
captum - Model interpretability and understanding for PyTorch
trulens - Evaluation and Tracking for LLM Experiments
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.
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