VariantModels: Error rates of supervised learning methods for variant...
In tdhock/animint2: Animated Interactive Grammar of Graphics
VariantModels R Documentation
Error rates of supervised learning methods for variant calling
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
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/animint2 documentation built on April 14, 2024, 4:22 p.m.
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| VariantModels | R Documentation |
Error rates of supervised learning methods for variant calling
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
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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