Select Uncorrelated Features

mlr3tuning tuning optimization tuning mlr3pipelines filtering iris data set classification

The following example describes a situation where we aim to remove correlated features. This in essence means, that we drop features until no features have a correlation higher then a given cutoff. This is often useful when we for example want to use linear models.

Martin Binder , Florian Pfisterer
02-25-2020

Post moved to mlr-org.com/gallery/2020-02-25-remove-correlated-features/.

Citation

For attribution, please cite this work as

Binder & Pfisterer (2020, Feb. 25). mlr3gallery: Select Uncorrelated Features. Retrieved from https://mlr3gallery.mlr-org.com/posts/2020-02-25-remove-correlated-features/

BibTeX citation

@misc{binder2020select,
  author = {Binder, Martin and Pfisterer, Florian},
  title = {mlr3gallery: Select Uncorrelated Features},
  url = {https://mlr3gallery.mlr-org.com/posts/2020-02-25-remove-correlated-features/},
  year = {2020}
}