Scientific Software
Updated 6 months ago

imodels — Peer-reviewed • Rank 21.0 • Science 100%

imodels: a python package for fitting interpretable models - Published in JOSS (2021)

Scientific Software
Updated 6 months ago

shapr — Peer-reviewed • Rank 15.0 • Science 93%

shapr: An R-package for explaining machine learning models with dependence-aware Shapley values - Published in JOSS (2019)

Artificial Intelligence and Machine Learning Earth and Environmental Sciences (40%) Economics (40%)
Scientific Software · Peer-reviewed
Scientific Software
Updated 6 months ago

SIRUS.jl — Peer-reviewed • Rank 8.0 • Science 98%

SIRUS.jl: Interpretable Machine Learning via Rule Extraction - Published in JOSS (2023)

Scientific Software · Peer-reviewed
Scientific Software
Updated 6 months ago

TSInterpret — Peer-reviewed • Rank 7.2 • Science 93%

TSInterpret: A Python Package for the Interpretability of Time Series Classification - Published in JOSS (2023)

Updated 5 months ago

mlm-bias • Rank 4.9 • Science 67%

Measuring Biases in Masked Language Models for PyTorch Transformers. Support for multiple social biases and evaluation measures.

Updated 5 months ago

grad-cam • Rank 23.6 • Science 46%

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Updated 5 months ago

gam • Rank 11.8 • Science 36%

GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations

Updated 6 months ago

dashai • Science 36%

DashAI provides a simple graphical user interface (GUI) that guides users through a step-by-step process through creating, training, and saving a model.

Updated 6 months ago

explainpolysvm • Science 44%

ExplainPolySVM is a python package to provide interpretation and explainability to Support Vector Machine models trained with polynomial kernels. The package can be used with any SVM model as long as the components of the model can be extracted.

Updated 6 months ago

hello-penguins • Science 26%

Machine learning experiments with the Palmer Penguins dataset

Updated 6 months ago

cctv • Science 49%

This is the code and data to replicate the analysis in Serebrennikov, Skougarevskiy (2023).

Updated 6 months ago

pcfi • Science 44%

Per Class Feature Importance (PCFI): an explainability method for decision tree classifiers.