JOSS Papers: Interpretable machine learning

Published Papers

published Published 5 months ago

ReLax: Efficient and Scalable Recourse Explanation Benchmarking using JAX

Python Jupyter Notebook SCSS

10.21105/joss.06567

published Published 9 months 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 over 1 year ago

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

R

10.21105/joss.05587

published Published over 1 year ago

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

Python

10.21105/joss.05873

published Published over 1 year ago

SIRUS.jl: Interpretable Machine Learning via Rule Extraction

Julia

10.21105/joss.05786

published Published almost 4 years ago

imodels: a python package for fitting interpretable models

Python Jupyter Notebook

10.21105/joss.03192

published Published about 5 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 over 5 years ago

modelStudio: Interactive Studio with Explanations for ML Predictive Models

R JavaScript

10.21105/joss.01798

published Published almost 6 years ago

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

R

10.21105/joss.01444

published Published over 6 years ago

iRF: extracting interactions from random forests

R C++ C

10.21105/joss.01077

Displaying papers 1 - 10 of 12 in total

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