daltoolbox (version 1.2.747)

cla_svm: SVM for classification

Description

Support Vector Machines (SVM) for classification using e1071::svm.

Usage

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

Value

returns a SVM classification object

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)

Details

SVMs find a maximum‑margin hyperplane in a transformed feature space defined by a kernel (linear, radial, polynomial, sigmoid). The cost controls the trade‑off between margin width and training error; epsilon affects stopping; kernel sets the feature map.

References

Cortes, C. and Vapnik, V. (1995). Support-Vector Networks. Machine Learning 20(3):273–297. Chang, C.-C. and Lin, C.-J. (2011). LIBSVM: A library for support vector machines.

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