xplainable
facet
| xplainable | facet | |
|---|---|---|
| 2 | 5 | |
| 52 | 471 | |
| - | - | |
| 9.0 | 5.6 | |
| 14 days ago | 10 months ago | |
| Python | Jupyter Notebook | |
| GNU Affero General Public License v3.0 | 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.
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).
facet
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/r/technology top posts: Mar 1, 2021
FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.\ (0 comments)
- FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.
- Human-Explainable AI
- Facet: ML model inspection and model-based simulation for better explanations
What are some alternatives?
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
transient_rotordynamic - transient dynamics of elastic rotors in journal bearings with Julia and Python
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
wordlescraper - Combine wordle statistics metrics from various locations, data science to correlate scores with words, and a front end to display the results.
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).