JOSS Papers: Interpretable machine learning

Published Papers

published Published 3 months ago

CCS-Lib: A Python package to elicit latent knowledge from LLMs

Python

10.21105/joss.06511

published Published about 1 year ago

ReLax: Efficient and Scalable Recourse Explanation Benchmarking using JAX

Python Jupyter Notebook SCSS

10.21105/joss.06567

published Published over 1 year ago

TelescopeML -- I. An End-to-End Python Package for Interpreting Telescope Datasets through Training Machine Learning Models, Generating Statistical Reports, and Visualizing Results

Python

@EhsanGharibNezhad

10.21105/joss.06346

published Published about 2 years ago

CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects

R

10.21105/joss.05587

published Published about 2 years ago

pudu: A Python library for agnostic feature selection and explainability of Machine Learning spectroscopic problems

Python

10.21105/joss.05873

published Published over 2 years ago

SIRUS.jl: Interpretable Machine Learning via Rule Extraction

Julia

10.21105/joss.05786

published Published over 4 years ago

imodels: a python package for fitting interpretable models

Python Jupyter Notebook

10.21105/joss.03192

published Published almost 6 years ago

shapr: An R-package for explaining machine learning models with dependence-aware Shapley values

R Python C++

10.21105/joss.02027

published Published about 6 years ago

modelStudio: Interactive Studio with Explanations for ML Predictive Models

R JavaScript

10.21105/joss.01798

published Published over 6 years ago

modelDown: automated website generator with interpretable documentation for predictive machine learning models

R

10.21105/joss.01444

Displaying papers 1 - 10 of 13 in total

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