PatientLevelPrediction (version 6.4.1)

setSVM: Create setting for the python sklearn SVM (SVC function)

Description

Create setting for the python sklearn SVM (SVC function)

Usage

setSVM(
  C = list(1, 0.9, 2, 0.1),
  kernel = list("rbf"),
  degree = list(1, 3, 5),
  gamma = list("scale", 1e-04, 3e-05, 0.001, 0.01, 0.25),
  coef0 = list(0),
  shrinking = list(TRUE),
  tol = list(0.001),
  classWeight = list(NULL),
  cacheSize = 500,
  seed = sample(1e+05, 1)
)

Value

a modelSettings object

Arguments

C

(list) Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.

kernel

(list) Specifies the kernel type to be used in the algorithm. one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’. If none is given ‘rbf’ will be used.

degree

(list) degree of kernel function is significant only in poly, rbf, sigmoid

gamma

(list) kernel coefficient for rbf and poly, by default 1/n_features will be taken. ‘scale’, ‘auto’ or float, default=’scale’

coef0

(list) independent term in kernel function. It is only significant in poly/sigmoid.

shrinking

(list) whether to use the shrinking heuristic.

tol

(list) Tolerance for stopping criterion.

classWeight

(list) Class weight based on imbalance either 'balanced' or NULL

cacheSize

Specify the size of the kernel cache (in MB).

seed

A seed for the model

Examples

 # dontrun reason: requires python's scikit-learn, checkSklearn() will error without it 
if (FALSE) { 
plpData <- getEunomiaPlpData()
model <- setSVM(C = list(1), gamma = list("scale"), seed = 42)
saveLoc <- file.path(tempdir(), "svm")
results <- runPlp(plpData, modelSettings = model, saveDirectory = saveLoc)
# clean up
unlink(saveLoc, recursive = TRUE)
}