reg_svm: SVM for regression

In daltoolbox: Leveraging Experiment Lines to Data Analytics

SVM for regression

Description

Support Vector Regression (SVR) using e1071::svm.

Usage

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

Arguments

attribute	attribute target to model building
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

SVR optimizes a margin with an epsilon‑insensitive loss around the regression function. The cost controls regularization strength; epsilon sets the width of the insensitive tube; and kernel defines the feature map (linear, radial, polynomial, sigmoid).

Value

returns a SVM regression object

References

Drucker, H., Burges, C., Kaufman, L., Smola, A., Vapnik, V. (1997). Support Vector Regression Machines. Chang, C.-C. and Lin, C.-J. (2011). LIBSVM: A library for support vector machines.

Examples

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

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