SIRUS.jl
Interpretable Machine Learning via Rule Extraction (by rikhuijzer)
AIX360
Interpretability and explainability of data and machine learning models (by Trusted-AI)
SIRUS.jl | AIX360 | |
---|---|---|
1 | 2 | |
27 | 1,546 | |
- | 0.8% | |
8.3 | 8.2 | |
12 days ago | 3 months ago | |
Julia | Python | |
MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
SIRUS.jl
Posts with mentions or reviews of SIRUS.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-29.
-
SIRUS.jl: Interpretable Machine Learning via Rule Extraction
Version 1.2 of the SIRUS.jl package has just been registered. Since version 1.2, the package can be used for both classification and regression.
AIX360
Posts with mentions or reviews of AIX360.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-28.
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
-
[R] Explaining the Explainable AI: A 2-Stage Approach - Link to a free online lecture by the author in comments
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques https://arxiv.org/abs/1909.03012 https://github.com/Trusted-AI/AIX360
What are some alternatives?
When comparing SIRUS.jl and AIX360 you can also consider the following projects:
interpret - Fit interpretable models. Explain blackbox machine learning.
AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.