Resampling - Stratified, Blocked and Predefined

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When evaluating machine learning algorithms through resampling, it is preferable that each train/test partition will be a representative subset of the whole data set. This post covers three ways to achieve such reliable resampling procedures.

Milan Dragicevic , Giuseppe Casalicchio

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For attribution, please cite this work as

Dragicevic & Casalicchio (2020, March 30). mlr3gallery: Resampling - Stratified, Blocked and Predefined. Retrieved from

BibTeX citation

  author = {Dragicevic, Milan and Casalicchio, Giuseppe},
  title = {mlr3gallery: Resampling - Stratified, Blocked and Predefined},
  url = {},
  year = {2020}