cla_svm: SVM for classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

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")

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.

Value

returns a SVM classification object

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

daltoolbox documentation built on Nov. 5, 2025, 7:09 p.m.