JOSS Papers: interpretable machine learning

All Papers
2524
 Published Papers
2247
 Active Papers
277
published Published about 2 months ago

SIRUS.jl: Interpretable Machine Learning via Rule Extraction

Julia
@rikhuijzer
10.21105/joss.05786
published Published over 2 years ago

imodels: a python package for fitting interpretable models

Python Jupyter Notebook
@csinva
10.21105/joss.03192
published Published almost 4 years ago

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

R Python C++
@martinju
10.21105/joss.02027
published Published about 4 years ago

modelStudio: Interactive Studio with Explanations for ML Predictive Models

R JavaScript
@hbaniecki
10.21105/joss.01798
published Published over 4 years ago

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

R
@matiszak
10.21105/joss.01444
published Published almost 5 years ago

iRF: extracting interactions from random forests

R C++ C
@karlkumbier
10.21105/joss.01077
published Published about 5 years ago

survxai: an R package for structure-agnostic explanations of survival models

R
@AleksandraDabrowska
10.21105/joss.00961
published Published over 5 years ago

iml: An R package for Interpretable Machine Learning

R Jupyter Notebook
@christophM
10.21105/joss.00786
Displaying 8 papers

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ISSN 2475-9066