SVM.nfoldcv: Cross validation for SVM

In RMKL: Multiple Kernel Learning for Classification or Regression Problems

Description

This function performs cross validation to find best combinatiuon of hyper perameters and cost and uses this model to provide prediction performace results.

Usage

SVM.nfoldcv(data, outcome, train.samples, C, kernels, degree, scale, sigma,
  nfold = 10)

Arguments

data	List of data matrices for each pathways for each pathway
outcome	Binary outcome variable for MKL
train.samples	Vector of indices that will be used as training samples
C	Vector of canduidate cost parameters
kernels	vector of kernel types
degree	Degree of polynomial kernel matrix
scale	Leading coefficient on the polynomial kernel
sigma	Hyperparameter for the radial basis kernel
nfold	Number of folds used in cross validation

Value

cm Confustion matrix along with a variety of accuracy statistics

best.model Model that had the highest accuracy with nfold cross validation

Examples

data(benchmark.data)
example.data=benchmark.data[[1]]
training.samples=sample(1:nrow(example.data), floor(0.7*nrow(example.data)),replace=FALSE)
C=2^c(-1,1)
#Find the best cost parameter within the provided range if a linear kernel is used
SVM.nfoldcv(example.data[,1:2], as.factor(example.data[,3]),training.samples,C,
            'linear',0,0,0,nfold=3)

RMKL documentation built on May 2, 2019, 7:55 a.m.