PERSIMUNE/explainer
ExplaineR is an R package built for enhanced interpretation of classification and regression models based on SHAP method and interactive visualizations with unique functionalities so please feel free to check it out, See ExplaineR paper at doi:10.1093/bioadv/vbae049
This tool helps researchers, data scientists, and analysts understand how complex classification and regression models make their predictions. You feed in your trained machine learning model and the data it was trained on, and it outputs detailed explanations of feature importance and interactive visualizations that reveal why a model made a specific prediction or how it behaves for different groups of individuals. It's designed for anyone who needs to interpret and explain their predictive models to stakeholders or for scientific rigor.
No commits in the last 6 months.
Use this if you need to deeply understand why your machine learning model makes certain predictions, evaluate its fairness across different subgroups, or present clear, interactive explanations to a non-technical audience.
Not ideal if you are looking for a tool to build or train machine learning models, as its primary purpose is interpretation rather than model development.
Stars
21
Forks
1
Language
R
License
—
Last pushed
Aug 17, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/PERSIMUNE/explainer"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
obss/sahi
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
tensorflow/tcav
Code for the TCAV ML interpretability project
MAIF/shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent...
TeamHG-Memex/eli5
A library for debugging/inspecting machine learning classifiers and explaining their predictions
csinva/imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling...