SVM_predict {DMTL}  R Documentation 
Predictive Modeling using Support Vector Machine
Description
This function trains a Support Vector Machine regressor using the training
data provided and predict response for the test features. This implementation
depends on the kernlab
package.
Usage
SVM_predict(
x_train,
y_train,
x_test,
lims,
kernel = "rbf",
optimize = FALSE,
C = 2,
kpar = list(sigma = 0.1),
eps = 0.01,
seed = NULL,
verbose = FALSE,
parallel = FALSE
)
Arguments
x_train 
Training features for designing the SVM regressor. 
y_train 
Training response for designing the SVM regressor. 
x_test 
Test features for which response values are to be predicted.
If 
lims 
Vector providing the range of the response values for modeling. If missing, these values are estimated from the training response. 
kernel 
Kernel function for SVM implementation. The available options
are 
optimize 
Flag for model tuning. If 
C 
Cost of constraints violation. This is the constant "C" of the
regularization term in the Lagrange formulation. Defaults to 
kpar 
List of kernel parameters. This is a named list that contains the parameters to be used with the specified kernel. The valid parameters for the existing kernels are 
Valid only when 
eps 
The insensitiveloss function used for epsilonSVR. Defaults to

seed 
Seed for random number generator (for reproducible outcomes).
Defaults to 
verbose 
Flag for printing the tuning progress when 
parallel 
Flag for allowing parallel processing when performing grid
search i.e., 
Value
If x_test
is missing, the trained SVM regressor.
If x_test
is provided, the predicted values using the model.
Note
The response values are filtered to be bound by range in lims
.
Examples
set.seed(86420)
x < matrix(rnorm(3000, 0.2, 1.2), ncol = 3); colnames(x) < paste0("x", 1:3)
y < 0.3*x[, 1] + 0.1*x[, 2]  x[, 3] + rnorm(1000, 0, 0.05)
## Get the model only...
model < SVM_predict(x_train = x[1:800, ], y_train = y[1:800], kernel = "rbf")
## Get predictive performance...
y_pred < SVM_predict(x_train = x[1:800, ], y_train = y[1:800], x_test = x[801:1000, ])
y_test < y[801:1000]
print(performance(y_test, y_pred, measures = "RSQ"))