Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
mlr: Machine Learning in R
Authors: Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The mlr package... targets practitioners who want to quickly apply machine learning algorithms, as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment. The following example demonstrates the use of mlr. After loading required packages and the Sonar data set (Line 1)... optimizes for mean misclassification error (mmce). |
| Researcher Affiliation | Academia | Bernd Bischl EMAIL Michel Lang EMAIL Lars Kotthoff EMAIL Julia Schiffner EMAIL Jakob Richter EMAIL Erich Studerus EMAIL Giuseppe Casalicchio EMAIL Zachary M. Jones EMAIL Department of Statistics Ludwig-Maximilians-University Munich |
| Pseudocode | Yes | The following example demonstrates the use of mlr... 1 library(mlr); library(mlbench); data(Sonar) 2 task = make Classif Task(data=Sonar , target="Class") 3 lrn = make Learner("classif.ksvm") ... 13 res = tune Params(lrn , task , rdesc , par.set=ps , control=ctrl , measures=mmce) |
| Open Source Code | Yes | The mlr source code is available under the BSD 2-clause license and hosted on Git Hub (https://github.com/mlr-org/mlr). |
| Open Datasets | Yes | After loading required packages and the Sonar data set (Line 1), we create a classification task... 1 library(mlr); library(mlbench); data(Sonar) |
| Dataset Splits | Yes | The resample description tells mlr to use a 5-fold cross-validation (Line 4). 4 rdesc = make Resample Desc (method="CV", iters =5) |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or specific cloud instances) are provided in the paper. |
| Software Dependencies | Yes | Benchmarking and Parallelization. The benchmark function evaluates the performance of multiple learners on multiple tasks. As benchmark studies can quickly become very resource-demanding, mlr natively supports parallelization through the parallel Map package (Bischl and Lang, 2015) that can use local multicore, socket, and MPI computation modes... B. Bischl and M. Lang. parallel Map: Unified interface to some popular parallelization backends for interactive usage and package development, 2015. URL https://github.com/ berndbischl/parallel Map. R package version 1.3. |
| Experiment Setup | Yes | Hyperparameters and box-constraints for tuning are specified in Lines 5-11... make Discrete Param ("kernel", values=c("polydot", "rbfdot")), make Numeric Param ("C", lower =-15, upper =15, trafo=function(x) 2 x), make Numeric Param ("sigma", lower =-15, upper =15, trafo=function(x) 2 x, requires = quote(kernel == "rbfdot")), make Integer Param ("degree", lower = 1, upper = 5, requires = quote(kernel == "polydot")) and We use random search with at most 50 evaluations (Line 12)... Line 13 binds everything together and optimizes for mean misclassification error (mmce). |