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 | Archive PDF | Plain Text | 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). |