Provides functionality to perform machine-learning-based modeling in a computation pipeline. Its functions contain the basic steps of machine-learning-based knowledge discovery workflows, including model training and optimization, model evaluation, and model testing. To perform these tasks, the package builds heavily on existing machine-learning packages, such as 'caret' <https://github.com/topepo/caret/> and associated packages. The package can train multiple models, optimize model hyperparameters by performing a grid search or a random search, and evaluates model performance by different metrics. Models can be validated either on a test data set, or in case of a small sample size by k-fold cross validation or repeated bootstrapping. It also allows for 0-Hypotheses generation by performing permutation experiments. Additionally, it offers methods of model interpretation and item categorization to identify the most informative features from a high dimensional data space. The functions of this package can easily be integrated into computation pipelines (e.g. 'nextflow' <https://www.nextflow.io/>) and hereby improve scalability, standardization, and re-producibility in the context of machine-learning.
Reference manual: | flowml.pdf |
Package source: | flowml_0.1.3.tar.gz |
Windows binaries: | r-devel: flowml_0.1.3.zip, r-release: flowml_0.1.3.zip, r-oldrel: flowml_0.1.3.zip |
macOS binaries: | r-release (arm64): flowml_0.1.3.tgz, r-oldrel (arm64): flowml_0.1.3.tgz, r-release (x86_64): flowml_0.1.3.tgz, r-oldrel (x86_64): flowml_0.1.3.tgz |
Old sources: | flowml archive |
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