Description Usage Arguments Value Note Examples
View source: R/predictive_modeling.R
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
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 insensitive-loss function used for epsilon-SVR. 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., |
If x_test
is missing, the trained SVM regressor.
If x_test
is provided, the predicted values using the model.
The response values are filtered to be bound by range in lims
.
1 2 3 4 5 6 7 8 9 10 11 | 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"))
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