shapash
interpret
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shapash | interpret | |
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8 | 6 | |
2,642 | 5,998 | |
1.3% | 1.4% | |
8.6 | 9.7 | |
about 1 month ago | 3 days ago | |
Jupyter Notebook | C++ | |
Apache License 2.0 | MIT License |
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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?
interpret
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[D] Alternatives to the shap explainability package
Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods.
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What Are the Most Important Statistical Ideas of the Past 50 Years?
You may also find Explainable Boosting Machines interesting: https://github.com/interpretml/interpret
They're a bit like a best of both worlds between linear models and random forests (generalized additive models fit with boosted decision trees)
Disclosure: I helped build this open source package
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[N] Google confirms DeepMind Health Streams project has been killed off
Microsoft Explainable Boosting Machine (which is a Gaussian Additive Model and not a Gradient Boosted Trees 🙄 model) is a step in that direction https://github.com/interpretml/interpret
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[Discussion] XGBoost is the way.
Also I'd recommend everyone who works with xgboost to give EBM's a try! They perform comparably (except in the case of extreme interactions) but are actually interpretable! https://github.com/interpretml/interpret/ Beside that they since on runtime they're practically a lookup table they're very quick (at the cost of longer training time).
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[D] Generalized Additive Models… with trees?
Open source code by Microsoft: https://github.com/interpretml/interpret (called EBM in this implementation).
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Machine Learning with Medical Data (unbalanced dataset)
If it's not an image, have a go at Microsoft's Explainable Boosting Maching) https://github.com/interpretml/interpret which is not a GBM but a GAM (Gradient Boosting Machine vs Gradient Additive Model). This will also give you explanation via SHAP or LIME values.
What are some alternatives?
shap - A game theoretic approach to explain the output of any machine learning model.
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
alibi - Algorithms for explaining machine learning models
GlassCode - This plugin allows you to make JetBrains IDEs to be fully transparent while keeping the code sharp and bright.
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
trulens - Evaluation and Tracking for LLM Experiments
medspacy - Library for clinical NLP with spaCy.
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest
eurybia - âš“ Eurybia monitors model drift over time and securizes model deployment with data validation
DashBot-3.0 - Geometry Dash bot to play & finish levels - Now training much faster!