Polynomial degree 3 SVM Implements a polynomial degree 3 SVM using the general svm function (for ease of use in feature selection)

Polynomial degree 3 SVM Implements a polynomial degree 3 SVM using the general svm function (for ease of use in feature selection)

svmpolynomial3(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))
svmpolynomial3(data_train,data_test)
#> $test
#> [1] 0.625
#> 
#> $testsensitivity
#> [1] 0
#> 
#> $testspecificity
#> [1] 0
#> 
#> $training
#> [1] 0.6
#> 
#> $trainsensitivity
#> [1] 0
#> 
#> $trainspecificity
#> [1] 0
#>