exSVM: Expectile Regression

In liquidSVM: A Fast and Versatile SVM Package

Description

This routine performs non-parametric, asymmetric least squares regression using SVMs. The tested estimators are therefore estimating the conditional tau-expectiles of Y given X. By default, estimators for five different tau values are computed. svmExpectileRegression is a simple alias of exSVM.

Usage

exSVM(x, y, ..., weights = c(0.05, 0.1, 0.5, 0.9, 0.95), clipping = -1,
  do.select = TRUE)

svmExpectileRegression(x, y, ..., weights = c(0.05, 0.1, 0.5, 0.9, 0.95),
  clipping = -1, do.select = TRUE)

Arguments

x	either a formula or the features
y	either the data or the labels corresponding to the features x. It can be a character in which case the data is loaded using liquidData. If it is of type liquidData then after training and selection the model is tested using the testing data (y$test).
...	configuration parameters, see Configuration. Can be threads=2, display=1, gpus=1, etc.
weights	the expectiles that should be estimated
clipping	absolute value where the estimated labels will be clipped. -1 (the default) leads to an adaptive clipping value, whereas 0 disables clipping.
do.select	if TRUE also does the whole selection for this model

Value

an object of type svm. Depending on the usage this object has also $train_errors, $select_errors, and $last_result properties.

Examples

## Not run: 
tt <- ttsplit(quakes)
model <- exSVM(mag~., tt$train, display=1)
result <- test(model, tt$test)

errors(result)[2] ## is the same as
mean(ifelse(result[,2]<tt$test$mag, .1,.9) * (result[,2]-tt$test$mag)^2)

## End(Not run)

liquidSVM documentation built on Sept. 15, 2019, 1:02 a.m.