mlbench: Machine Learning Benchmark Problems. A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Alquier, Pierre; Ridgway, James; Chopin, Nicolas: On the properties of variational approximations of Gibbs posteriors (2016)
- Tutz, Gerhard; Koch, Dominik: Improved nearest neighbor classifiers by weighting and selection of predictors (2016)
- Cichosz, Paweł: Data mining algorithms. Explained using R (2015)
- Ishwaran, Hemant: The effect of splitting on random forests (2015)
- Chen, Yu-Chuan; Ha, Hyejung; Kim, Hyunjoong; Ahn, Hongshik: Canonical forest (2014)
- Lee, Alan; Willcox, Bobby: Minkowski generalizations of Ward’s method in hierarchical clustering (2014)
- Bischl, Bernd; Schiffner, Julia; Weihs, Claus: Benchmarking local classification methods (2013)
- Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
- Sexton, Joseph; Laake, Petter: Boosted coefficient models (2012)
- Adler, Werner; Potapov, Sergej; Lausen, Berthold: Classification of repeated measurements data using tree-based ensemble methods (2011)
- Hothorn, Torsten; Lausen, Berthold: Bundling classifiers by bagging trees (2005)