setSVM: Create setting for SVM with python

In hxia/plp-git-demo: Package for patient level prediction using data in the OMOP Common Data Model

Description

Create setting for SVM with python

Usage

setSVM(
  kernel = "rbf",
  C = c(1, 0.9, 2, 0.1),
  degree = c(1, 3, 5),
  gamma = c(1e-04, 3e-05, 0.001, 0.01, 0.25),
  shrinking = T,
  coef0 = 0,
  classWeight = T,
  seed = NULL
)

Arguments

C	penalty parameter C of the error term.
degree	degree of kernel function is significant only in poly, rbf, sigmoid
gamma	kernel coefficient for rbf and poly, by default 1/n_features will be taken.
shrinking	wether to use the shrinking heuristic.
coef0	independent term in kernel function. It is only significant in poly/sigmoid.
classWeight	Class weight based on imbalance
seed	A seed for the model
kernal	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.

Examples

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

## End(Not run)

hxia/plp-git-demo documentation built on March 19, 2021, 1:54 a.m.