VariantModels: Error rates of supervised learning methods for variant...
In tdhock/animint: Interactive animations
Description
Usage
Format
Source
Description
Several supervised machine learning models were applied to the binary
classification task of predicting True Positive or False Positive
variants, using several filtering scores as input.
Usage
1
data("VariantModels")
Format
List of 6 data.frames: roc, auc, error, ranks, thresholds, minima.
Source
https://github.com/tdhock/malaria-evolution-viz/blob/master/figure-folds.R
tdhock/animint documentation built on July 27, 2019, 5:57 a.m.
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Description Usage Format Source
Description
Several supervised machine learning models were applied to the binary classification task of predicting True Positive or False Positive variants, using several filtering scores as input.
Usage
1 | data("VariantModels")
|
Format
List of 6 data.frames: roc, auc, error, ranks, thresholds, minima.
Source
https://github.com/tdhock/malaria-evolution-viz/blob/master/figure-folds.R
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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