Construct a learner

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.

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))
#>            1            2            3            4            5            6 
#>  1.995377303  0.645702513  0.005660922 -2.727438622  1.112411111  1.060965475 

# 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
#>         1         2         3         4         5         6 
#> 0.1837071 0.2929537 0.5175953 0.9222461 0.1874361 0.2446923 
lr$predict(head(d), probability = FALSE) # predict labels
#> 1 2 3 4 5 6 
#> 1 1 1 0 1 1 
#> Levels: 0 1

# multi-class classification
lr <- learner_svm(Species ~ .)
lr$estimate(iris)
lr$predict(head(iris))
#>      setosa versicolor   virginica
#> 1 0.9811383 0.01047810 0.008383591
#> 2 0.9741682 0.01682844 0.009003326
#> 3 0.9798900 0.01105912 0.009050920
#> 4 0.9760581 0.01421695 0.009724906
#> 5 0.9803570 0.01077415 0.008868896
#> 6 0.9752374 0.01555128 0.009211281