Description Usage Arguments Value Examples
View source: R/hyperparameters.R
Tuning SVM kernel. Trains SVMs with a range of kernels (linear, polynomial degree 2, 3 and 4, radial and sigmoid) using cross validation so the optimal kernel can be chosen (using the resulting plots). If specified (by showplots=FALSE) the plots are saved as jpegs.
1 | selectsvmkernel(data, title, showplots = TRUE, output_prefix = "")
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data |
Dataset: dataframe containing classification column and all other column features. Both the training and test datasets will be taken from this dataset. |
title |
Title to be used for the resulting boxplot |
showplots |
TRUE if plots should be shown in standard output, FALSE is plots should be saved as jpg files. |
output_prefix |
Prefix used for saving plots. If showplots==FALSE then plots are saved here. Otherwise, standard output. |
Dataframe containing test and training accuracy, sensitivity and specificity
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