SL.ksvm {SuperLearner}

Wrapper for Kernlab's SVM algorithm

Description

Wrapper for Kernlab's support vector machine algorithm.

Usage

SL.ksvm(Y, X, newX, family, type = NULL, kernel = "rbfdot",
  kpar = "automatic", scaled = T, C = 1, nu = 0.2, epsilon = 0.1,
  cross = 0, prob.model = family$family == "binomial",
  class.weights = NULL, cache = 40, tol = 0.001, shrinking = T, ...)

Arguments

Y

Outcome variable

X

Training dataframe

newX

Test dataframe

family

Gaussian or binomial

type

ksvm can be used for classification , for regression, or for novelty detection. Depending on whether y is a factor or not, the default setting for type is C-svc or eps-svr, respectively, but can be overwritten by setting an explicit value. See ?ksvm for more details.

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes the inner product in feature space between two vector arguments. See ?ksvm for more details.

kpar

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. See ?ksvm for more details.

scaled

A logical vector indicating the variables to be scaled. If scaled is of length 1, the value is recycled as many times as needed and all non-binary variables are scaled. Per default, data are scaled internally (both x and y variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions.

C

cost of constraints violation (default: 1) this is the 'C'-constant of the regularization term in the Lagrange formulation.

nu

parameter needed for nu-svc, one-svc, and nu-svr. The nu parameter sets the upper bound on the training error and the lower bound on the fraction of data points to become Support Vectors (default: 0.2).

epsilon

epsilon in the insensitive-loss function used for eps-svr, nu-svr and eps-bsvm (default: 0.1)

cross

if a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model: the accuracy rate for classification and the Mean Squared Error for regression

prob.model

if set to TRUE builds a model for calculating class probabilities or in case of regression, calculates the scaling parameter of the Laplacian distribution fitted on the residuals. Fitting is done on output data created by performing a 3-fold cross-validation on the training data. (default: FALSE)

class.weights

a named vector of weights for the different classes, used for asymmetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named.

cache

cache memory in MB (default 40)

tol

tolerance of termination criterion (default: 0.001)

shrinking

option whether to use the shrinking-heuristics (default: TRUE)

...

Any additional parameters, not currently passed through.

Value

List with predictions and the original training data & hyperparameters.

References

Hsu, C. W., Chang, C. C., & Lin, C. J. (2016). A practical guide to support vector classification. https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

Scholkopf, B., & Smola, A. J. (2001). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.

Vapnik, V. N. (1998). Statistical learning theory (Vol. 1). New York: Wiley.

Zeileis, A., Hornik, K., Smola, A., & Karatzoglou, A. (2004). kernlab-an S4 package for kernel methods in R. Journal of statistical software, 11(9), 1-20.

See Also

predict.SL.ksvm ksvm predict.ksvm

Examples

data(Boston, package = "MASS")
Y = Boston$medv
# Remove outcome from covariate dataframe.
X = Boston[, -14]

set.seed(1)

sl = SuperLearner(Y, X, family = gaussian(),
                 SL.library = c("SL.mean", "SL.ksvm"))
sl

pred = predict(sl, X)
summary(pred$pred)