Wrapper for Kernlab's support vector machine algorithm.
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, ...)Outcome variable
Training dataframe
Test dataframe
Gaussian or binomial
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.
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.
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.
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.
cost of constraints violation (default: 1) this is the 'C'-constant of the regularization term in the Lagrange formulation.
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 in the insensitive-loss function used for eps-svr, nu-svr and eps-bsvm (default: 0.1)
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
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)
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 memory in MB (default 40)
tolerance of termination criterion (default: 0.001)
option whether to use the shrinking-heuristics (default: TRUE)
Any additional parameters, not currently passed through.
List with predictions and the original training data & hyperparameters.
Hsu, C. W., Chang, C. C., & Lin, C. J. (2016). A practical guide to support vector classification. http://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.
# NOT RUN {
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)
# }
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