yellowbrick
shapash
| yellowbrick | shapash | |
|---|---|---|
| 2 | 8 | |
| 4,201 | 2,648 | |
| 0.4% | 0.7% | |
| 2.8 | 8.6 | |
| 10 months ago | 4 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.
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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.
yellowbrick
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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Any interesting open projects to join? Or anyone want with some good ideas want to start one?
I have contributed to Yellowbrick in the past. https://github.com/DistrictDataLabs/yellowbrick/
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?
kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
shap - A game theoretic approach to explain the output of any machine learning model.
Anaconda - Anaconda turns your Sublime Text 3 in a full featured Python development IDE including autocompletion, code linting, IDE features, autopep8 formating, McCabe complexity checker Vagrant and Docker support for Sublime Text 3 using Jedi, PyFlakes, pep8, MyPy, PyLint, pep257 and McCabe that will never freeze your Sublime Text 3
interpret - Fit interpretable models. Explain blackbox machine learning.
itermplot - An awesome iTerm2 backend for Matplotlib, so you can plot directly in your terminal.
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
seaborn-image - High-level API for attractive and descriptive image visualization in Python
GlassCode - This plugin allows you to make JetBrains IDEs to be fully transparent while keeping the code sharp and bright.
fpdf2 - Simple PDF generation for Python
trulens - Evaluation and Tracking for LLM Experiments
scikit-survival - Survival analysis built on top of scikit-learn
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms