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We've compiled a list of 5 free and paid alternatives to R mlr. The primary competitors include H2O.ai, R Caret. In addition to these, users also draw comparisons between R mlr and ML.NET, python auto-sklearn, R MLstudio. Also you can look at other similar options here: About.
We've compiled a list of 5 free and paid alternatives to R mlr. The primary competitors include H2O.ai, R Caret. In addition to these, users also draw comparisons between R mlr and ML.NET, python auto-sklearn, R MLstudio. Also you can look at other similar options here: About.
H2O is an open source, in-memory, distributed, fast, and scalable machine learning and predictive...
The caret package (short for _C_lassification _A_nd _RE_gression _T_raining) is a set of functions...
The ML Studio is interactive for EDA, statistical modeling and machine learning applications.
Machine Learning in R: mlr, a framework for machine learning experiments in R.
Machine Learning in R: mlr, a framework for machine learning experiments in R.
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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
Machine Learning
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