shap VS shapash

Compare shap vs shapash and see what are their differences.

shap

A game theoretic approach to explain the output of any machine learning model. (by shap)

shapash

đź”… Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models (by MAIF)
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shap shapash
38 8
21,389 2,629
1.6% 2.4%
9.4 8.6
6 days ago 6 days ago
Jupyter Notebook Jupyter Notebook
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.

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 2023-12-06.

shapash

Posts with mentions or reviews of shapash. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

What are some alternatives?

When comparing shap and shapash you can also consider the following projects:

Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

captum - Model interpretability and understanding for PyTorch

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

interpret - Fit interpretable models. Explain blackbox machine learning.

LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)

awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

anchor - Code for "High-Precision Model-Agnostic Explanations" paper

articulated-animation - Code for Motion Representations for Articulated Animation paper

lucid - A collection of infrastructure and tools for research in neural network interpretability.

GlassCode - This plugin allows you to make JetBrains IDEs to be fully transparent while keeping the code sharp and bright.

trulens - Evaluation and Tracking for LLM Experiments