Skip to contents

Summarize a fitted LS-SVR with RMSPE loss

summary.psvr_rmspe.Rd

Prints the kernel, the training count, the hyperparameter, and whether the diag(1/y) preconditioner fired. No support-vector count is reported: LS-SVR performs no pruning, so every training point contributes to the prediction.

Usage

# S3 method for class 'psvr_rmspe'
summary(object, ...)

Arguments

object

An object of class "psvr_rmspe", from psvr_rmspe() with sym_type = "none", or from a parsnip fit unwrapped with parsnip::extract_fit_engine().

...

Ignored.

Value

object, invisibly. Called for the printed summary.

See also

print.psvr_rmspe(), coef.psvr_rmspe()

Examples

set.seed(1)
X <- matrix(rnorm(40), 20, 2)
y <- rlnorm(20)
fit <- psvr_rmspe(X, y, kernel = make_kernel("rbf", sigma = 1), gamma = 100)
summary(fit)
#> 
#> LS-SVR with RMSPE loss  [psvr_rmspe]
#> 
#>   Kernel:          RBF (sigma = 1)
#>   Training obs.:   20
#>   Predictors:      2
#> 
#>   Hyperparameters:
#>     Gamma  = 100
#> 
#>   Preconditioner:  applied (diag(1/y) symmetric rescaling)
#>