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Summarize a fitted epsilon-SVR with MAPE loss

summary.psvr_mape.Rd

Prints the kernel, the training and support-vector counts, the hyperparameters, and the SMO iteration count with its convergence status. Every training point contributes for LS-SVR but not here: the support-vector percentage is the sparsity of the fit.

Usage

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

Arguments

object

An object of class "psvr_mape", from psvr_mape() 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_mape(), coef.psvr_mape()

Examples

set.seed(1)
X <- matrix(rnorm(40), 20, 2)
y <- rlnorm(20)
fit <- psvr_mape(X, y, kernel = make_kernel("rbf", sigma = 1),
                 C = 10, eps = 5)
summary(fit)
#> 
#> Epsilon-SVR with MAPE loss  [psvr_mape]
#> 
#>   Kernel:          RBF (sigma = 1)
#>   Training obs.:   20
#>   Predictors:      2
#>   Support vectors: 20 (100.0%)
#> 
#>   Hyperparameters:
#>     C      = 10
#>     eps    = 5
#> 
#>   SMO iterations:  3039 (converged)
#>