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

In OHDSI/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

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)
)

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

## Not run: 
model.svm <- setSVM(kernel='rbf', seed = NULL)

## End(Not run)

OHDSI/PatientLevelPrediction documentation built on April 27, 2024, 8:11 p.m.