mlr3
R package mlr3: Machine Learning in R - Next Generation. Efficient, object-oriented programming on the building blocks of machine learning. Provides ’R6’ objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While ’mlr3’ focuses on the core computational operations, add-on packages provide additional functionality.
Keywords for this software
References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
Sorted by year (- Hurley, Catherine B.; O’Connell, Mark; Domijan, Katarina: Interactive slice visualization for exploring machine learning models (2022)
- Binder, Martin; Pfisterer, Florian; Lang, Michel; Schneider, Lennart; Kotthoff, Lars; Bischl, Bernd: mlr3pipelines -- flexible machine learning pipelines in R (2021)
- Florian Pfisterer, Christoph Kern, Susanne Dandl, Matthew Sun, Michael P. Kim, Bernd Bischl: mcboost: Multi-Calibration Boosting for R (2021) not zbMATH
- Jin Zhu, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu, Xueqin Wang: abess: A Fast Best Subset Selection Library in Python and R (2021) arXiv
- Miron B. Kursa: Praznik: High performance information-based feature selection (2021) not zbMATH
- Travis-Lumer, Yael; Goldberg, Yair: Kernel machines for current status data (2021)
- Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
- Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek: Landscape of R packages for eXplainable Artificial Intelligence (2020) arXiv
- Michel Lang, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, Bernd Bischl: mlr3: A modern object-oriented machine learning framework in R (2019) not zbMATH