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R mlr

R mlr is an R package that provides a unified interface for classification, regression, survival analysis and clustering. It features automated machine learning with hyperparameter tuning, flexible feature preprocessing and model ensemble capabilities.

What is R mlr?

R mlr is an R package that provides a unified interface for classification, regression, survival analysis and clustering. It features:

  • Automated machine learning with hyperparameter tuning using various search strategies like grid search, random search, Bayesian optimization etc.
  • Support for over 60 learners from regression, classification, survival analysis, cluster analysis etc.
  • Flexible feature preprocessing with missing value imputation, scaling, generating interactions etc.
  • Model ensemble capabilities like stacking and bagging
  • Built-in resampling for robust performance estimation like cross-validation and bootstrapping
  • Feature selection and hyperparameter tuning capabilities
  • Easy integration with other R packages

Some key advantages of mlr are:

  • Unified interface to train and predict on various models
  • Automating machine learning pipeline consisting of preprocessing, model training, tuning and evaluation
  • Easily benchmark different models
  • Ensemble modeling capabilities
  • Detailed logging mechanisms

mlr is a popular package used widely for automated machine learning and model building in R.

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