shap VS awesome-production-machine-learning

Compare shap vs awesome-production-machine-learning and see what are their differences.

shap

A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap] (by slundberg)
Interpretability Machine Learning Deep Learning gradient-boosting Shap Shapley explainability

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awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning (by EthicalML)
Machine Learning Mlops Interpretability explainability responsible-ai Deep Learning machine-learning-operations ml-ops ml-operations privacy-preserving privacy-preserving-ml privacy-preserving-machine-learning Data Mining large-scale-ml production-ml large-scale-machine-learning production-machine-learning Awesome awesome-list
Source Code
ethical.institute
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InfluxDB - Power Real-Time Data Analytics at Scale
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SaaSHub helps you find the best software and product alternatives
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shap awesome-production-machine-learning
1 9
20,121 16,178
- 2.3%
10.0 7.5
8 months ago 2 days ago
Jupyter Notebook
MIT License MIT License
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shap

Posts with mentions or reviews of shap. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-18.

awesome-production-machine-learning

Posts with mentions or reviews of awesome-production-machine-learning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-13.

What are some alternatives?

When comparing shap and awesome-production-machine-learning you can also consider the following projects:

csgo-impact-rating - A probabilistic player rating system for Counter Strike: Global Offensive, powered by machine learning

shap - A game theoretic approach to explain the output of any machine learning model.

transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

awesome-jax - JAX - A curated list of resources https://github.com/google/jax

lime - Lime: Explaining the predictions of any machine learning classifier

netron - Visualizer for neural network, deep learning and machine learning models

awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

awesome-mlops - :sunglasses: A curated list of awesome MLOps tools

augmented-interpretable-models - Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.

awesome-ml-for-cybersecurity - :octocat: Machine Learning for Cyber Security

datascience - Curated list of Python resources for data science.

shap vs csgo-impact-rating awesome-production-machine-learning vs shap shap vs transformers-interpret awesome-production-machine-learning vs awesome-jax shap vs lime awesome-production-machine-learning vs netron shap vs awesome-shapley-value awesome-production-machine-learning vs awesome-mlops shap vs augmented-interpretable-models awesome-production-machine-learning vs awesome-ml-for-cybersecurity shap vs shap awesome-production-machine-learning vs datascience