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mthrun

DRquality:Quality Measurements for Dimensionality Reduction

Several quality measurements for investigating the performance of dimensionality reduction methods are provided here. In addition a new quality measurement called Gabriel classification error is made accessible.

Maintained by Michael Thrun. Last updated 2 years ago.

2.00 score

boehringer-ingelheim

flowml:A Backend for a 'nextflow' Pipeline that Performs Machine-Learning-Based Modeling of Biomedical Data

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.

Maintained by Sebastian Malkusch. Last updated 1 years ago.

1 stars 2.00 score

cran

EDOtrans:Euclidean Distance-Optimized Data Transformation

A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance.

Maintained by Jorn Lotsch. Last updated 12 months ago.

1.00 score

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