mlr

Machine Learning in R

CRAN Package

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.

Documentation

Team

Insights

Last 30 days

Last 365 days

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Data provided by CRAN

Binaries

Dependencies

  • Depends1 package
  • Imports11 packages
  • Suggests101 packages
  • Reverse Depends5 packages
  • Reverse Imports12 packages
  • Reverse Suggests15 packages
  • Reverse Enhances1 package