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.
Package details |
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Author | Sebastian Malkusch [aut, cre] (<https://orcid.org/0000-0001-6766-140X>), Kolja Becker [aut] (<https://orcid.org/0000-0001-8282-5329>), Alexander Peltzer [ctb] (<https://orcid.org/0000-0002-6503-2180>), Neslihan Kaya [ctb] (<https://orcid.org/0000-0002-0213-3072>), Boehringer Ingelheim Ltd. [cph, fnd] |
Maintainer | Sebastian Malkusch <sebastian.malkusch@boehringer-ingelheim.com> |
License | GPL (>= 3) |
Version | 0.1.3 |
URL | https://github.com/Boehringer-Ingelheim/flowml |
Package repository | View on CRAN |
Installation |
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