iml - Interpretable Machine Learning

Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) <doi:10.48550/arxiv.1801.01489>, accumulated local effects plots described by Apley (2018) <doi:10.48550/arxiv.1612.08468>, partial dependence plots described by Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime') described by Ribeiro et. al (2016) <doi:10.48550/arXiv.1602.04938>, the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>, feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.

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12.55 score 503 stars 3 dependents 1.1k scripts 7.2k downloads

OpenML - Open Machine Learning and Open Data Platform

We provide an R interface to 'OpenML.org' which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. See <https://www.openml.org/guide/api> for more information.

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arffbenchmarkingbenchmarking-suiteclassificationdata-sciencedatabasedatasetdatasetsmachine-learningmachine-learning-algorithmsopen-dataopen-scienceopendataopenmlopenscienceregressionreproducible-researchstatistics

11.07 score 99 stars 7.4k scripts 551 downloads

RBPcurve - The Residual-Based Predictiveness Curve

The RBP curve is a visual tool to assess the performance of prediction models.

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3.40 score 5 stars 1 scripts 191 downloads