AIX360
shapash
| AIX360 | shapash | |
|---|---|---|
| 2 | 8 | |
| 1,533 | 2,645 | |
| 2.0% | 0.6% | |
| 8.2 | 8.6 | |
| 2 months ago | 9 days ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | 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.
AIX360
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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[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
shapash
- GitHub - MAIF/shapash: Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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This A.I.-generated artwork, Théâtre D'opéra Spatial, won first place at an art competition, and the art community isn't happy about it
There's work being done in that regard (like this python module), but as far as I know it's very clearly statistical guesstimates, and though it "works", the mathematical foundations are still somewhat shaky. There are heuristics in there we can't get rid of for now. But it's still better than nothing. Waaaaaay better than nothing.
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Hacker News top posts: Jun 14, 2022
Shapash – Python library to make machine learning interpretable\ (4 comments)
- Shapash – Python library to make machine learning interpretable
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State of the Art data drift libraries on Python?
Try out eurybia, from the author of shapash which is a brilliant library as well.
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[P] It Is Now Possible To Generate a Model Audit Report with Shapash
With the new version of Shapash that is now available, you can document each model you release into production. Within a few lines of code, you can include in an HTML report all the information about your model (and its associated performance), the data it uses, its learning strategy, … this report is designed to be easily shared with a Data Protection Officer, an internal audit department, a risk control department, a compliance department, or anyone who wants to understand his work.
- [D] Has anyone ever used the SHAP and LIME models in machine learning?
What are some alternatives?
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.
shap - A game theoretic approach to explain the output of any machine learning model.
explainable-cnn - 📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
interpret - Fit interpretable models. Explain blackbox machine learning.
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
GlassCode - This plugin allows you to make JetBrains IDEs to be fully transparent while keeping the code sharp and bright.
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
trulens - Evaluation and Tracking for LLM Experiments
backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms