cla_svm: SVM for classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

SVM for classification

Description

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

returns 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

daltoolbox documentation built on Nov. 3, 2024, 9:06 a.m.