Creates a regression object that uses the Support Vector Machine (SVM) method for regression It wraps the e1071 and svm library.
reg_svm(attribute, epsilon = 0.1, cost = 10, kernel = "radial")returns a SVM regression object
attribute target to model building
parameter that controls the width of the margin around the separating hyperplane
parameter that controls the trade-off between having a wide margin and correctly classifying training data points
the type of kernel function to be used in the SVM algorithm (linear, radial, polynomial, sigmoid)
data(Boston)
model <- reg_svm("medv", epsilon=0.2,cost=40.000)
# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, Boston)
train <- sr$train
test <- sr$test
model <- fit(model, train)
test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics
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