learner_svm: Construct a learner

In targeted: Targeted Inference

Construct a learner

Description

Constructs a learner class object for fitting support vector machines with e1071::svm. As shown in the examples, the constructed learner returns predicted class probabilities of class 2 in case of binary classification. A ⁠n times p⁠ matrix, with n being the number of observations and p the number of classes, is returned for multi-class classification.

Usage

learner_svm(
  formula,
  info = "e1071::svm",
  cost = 1,
  epsilon = 0.1,
  kernel = "radial",
  learner.args = NULL,
  ...
)

Arguments

formula	(formula) Formula specifying response and design matrix.
info	(character) Optional information to describe the instantiated learner object.
cost	cost of constraints violation (default: 1)—it is the ‘C’-constant of the regularization term in the Lagrange formulation.
epsilon	epsilon in the insensitive-loss function (default: 0.1)
kernel	the kernel used in training and predicting. You might consider changing some of the following parameters, depending on the kernel type. linear: u'v polynomial: (\gamma u'v + coef0)^{degree} radial basis: e^(-\gamma |u-v|^2) sigmoid: tanh(\gamma u'v + coef0)
learner.args	(list) Additional arguments to learner$new().
...	Additional arguments to e1071::svm.

Value

learner object.

Examples

n <- 5e2
x1 <- rnorm(n, sd = 2)
x2 <- rnorm(n)
lp <- x2*x1 + cos(x1)
yb <- rbinom(n, 1, lava::expit(lp))
y <-  lp + rnorm(n, sd = 0.5**.5)
d <- data.frame(y, yb, x1, x2)

# regression
lr <- learner_svm(y ~ x1 + x2)
lr$estimate(d)
lr$predict(head(d))

# binary classification
lr <- learner_svm(as.factor(yb) ~ x1 + x2)
# alternative to transforming response variable to factor
# lr <- learner_svm(yb ~ x1 + x2, type = "C-classification")
lr$estimate(d)
lr$predict(head(d)) # predict class probabilities of class 2
lr$predict(head(d), probability = FALSE) # predict labels

# multi-class classification
lr <- learner_svm(Species ~ .)
lr$estimate(iris)
lr$predict(head(iris))

targeted documentation built on Jan. 12, 2026, 9:08 a.m.