R mlr Alternatives

R mlr

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