Linear SVM Implements a linear SVM using the general svm function (for ease of use in feature selection)

svmlinear(data_train, data_test, includeplot = FALSE)

Arguments

data_train

Training set: dataframe containing classification column and all other columns features. This is the dataset on which the decision tree model is trained.

data_test

Test set: dataframe containing classification column and all other columns features. This is the dataset on which the decision tree model in tested.

includeplot

Show performance scatter plot (default:FALSE)

Value

List containing performance percentages, accessed using training (training accuracy), test (test accuracy), trainsensitivity, testsensitivity, trainspecificity, testspecificity.

Examples

data_train = data.frame( classification=as.factor(c(1,1,0,0,1,1,0,0,1,1)), A=c(1,1,1,0,0,0,1,1,1,0), B=c(0,1,1,0,1,1,0,1,1,0), C=c(0,0,1,0,0,1,0,0,1,0)) data_test = data.frame( classification=as.factor(c(1,1,0,0,1,1,1,0)), A=c(0,0,0,1,0,0,0,1), B=c(1,1,1,0,0,1,1,1), C=c(0,0,1,1,0,0,1,1)) svmlinear(data_train,data_test)
#> $test #> [1] 0.625 #> #> $testsensitivity #> [1] 0 #> #> $testspecificity #> [1] 0 #> #> $training #> [1] 0.6 #> #> $trainsensitivity #> [1] 0 #> #> $trainspecificity #> [1] 0 #>