cla_svm {daltoolbox}

SVM for classification

Description

Creates a classification object that uses the Support Vector Machine (SVM) method for classification It wraps the e1071 library.

Usage

cla_svm(attribute, slevels, epsilon = 0.1, cost = 10, kernel = "radial")

Arguments

attribute

attribute target to model building

slevels

possible values for the target classification

epsilon

parameter that controls the width of the margin around the separating hyperplane

cost

parameter that controls the trade-off between having a wide margin and correctly classifying training data points

kernel

the type of kernel function to be used in the SVM algorithm (linear, radial, polynomial, sigmoid)

Value

A SVM classification object

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_svm("Species", slevels, epsilon=0.0,cost=20.000)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics