mlr provides this so that you can focus on your experiments! The framework provides supervised methods like classification, regression and survival analysis along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering. It is written in a way that you can extend it yourself or deviate from the implemented convenience methods and your own complex experiments. package is nicely connected to the OpenML R package , which aims at supporting collaborative machine learning online and allows to easily share datasets as well as machine learning tasks, algorithms and experiments. Clear S3 interface to R classification, regression, clustering and survival analysis methods Possibility to fit, predict, evaluate and resample models Easy extension mechanism through S3 inheritance Abstract description of learners and tasks by properties Parameter system for learners to encode data types and constraints Many convenience methods and generic building blocks for your machine learning experiments Resampling methods like bootstrapping, cross-validation and subsampling Extensive visualizations for e.g. ROC curves, predictions and partial predictions Benchmarking of learners for multiple data sets Easy hyperparameter tuning using different optimization strategies, including potent configurators like iterated F-racing (irace) or sequential model-based optimization Variable selection with filters and wrappers Nested resampling of models with tuning and feature selection Cost-sensitive learning, threshold tuning and imbalance correction Wrapper mechanism to extend learner functionality in complex and custom ways Combine different processing steps to a complex data mining chain that can be jointly optimized OpenML connector for the Open Machine Learning server Extension points to integrate your own stuff Parallelization is built-in Unit-testing