skcla_svc {daltoolboxdp} | R Documentation |
Support Vector Machine Classification
Description
Implements classification using Support Vector Machine (SVM) algorithm. This function wraps the SVC classifier from Python's scikit-learn library.
Usage
skcla_svc(
attribute,
slevels,
kernel = "rbf",
degree = 3,
gamma = "scale",
coef0 = 0,
tol = 0.001,
C = 1,
shrinking = TRUE,
probability = FALSE,
cache_size = 200,
class_weight = NULL,
verbose = FALSE,
max_iter = -1,
decision_function_shape = "ovr",
break_ties = FALSE,
random_state = NULL
)
Arguments
attribute |
Target attribute name for model building |
slevels |
List of possible values for classification target |
kernel |
Kernel function type ('linear', 'poly', 'rbf', 'sigmoid') |
degree |
Polynomial degree when using 'poly' kernel |
gamma |
Kernel coefficient value |
coef0 |
Independent term value in kernel function |
tol |
Tolerance value for stopping criterion |
C |
Regularization strength parameter |
shrinking |
Whether to use shrinking heuristic |
probability |
Whether to enable probability estimates |
cache_size |
Kernel cache size value in MB |
class_weight |
Weights associated with classes |
verbose |
Whether to enable verbose output |
max_iter |
Maximum number of iterations |
decision_function_shape |
Shape of decision function ('ovo', 'ovr') |
break_ties |
Whether to break tie decisions |
random_state |
Seed for random number generation |
Details
SVM Classifier
Value
An SVM classifier object
skcla_svc
object
Examples
#See an example of using `skcla_svc` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/cla_svm.md