mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines
Begüm D. Topçuoğlu, Zena Lapp, Kelly L. Sovacool, Evan S. Snitkin, Jenna Wiens, Patrick D. Schloss
Abstract
Machine learning (ML) for classification and prediction based on a set of features is used to make decisions in healthcare, economics, criminal justice and more. However, implementing an ML pipeline including preprocessing, model selection, and evaluation can be time-consuming, confusing, and difficult. Here, we present mikropml (prononced "meek-ROPE em el"), an easy-to-use R package that implements ML pipelines using regression, support vector machines, decision trees, random forest, or gradient-boosted trees. The package is available on GitHub, CRAN, and conda.
Topics & Concepts
Pipeline (software)Computer scienceRandom forestSupport vector machineDecision treePreprocessorRopeMachine learningArtificial intelligencePipeline transportSupervised learningSet (abstract data type)R packageSelection (genetic algorithm)Data pre-processingData miningEngineeringArtificial neural networkOperating systemProgramming languageAlgorithmEnvironmental engineeringMachine Learning and Data ClassificationData Analysis with RMachine Learning in Healthcare