Description Usage Arguments Value Examples
View source: R/prediction.Classification.R
This function creates gramm matrix for traning set baed upon several types of kernel and specified hyper paremeters. Matrix corresponds to similarity betwween each sample in the training set.
1 | prediction.Classification(model, ktest, train.outcome)
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model |
MKL model |
ktest |
Gramm matrix of training data and test data |
train.outcome |
Outcome for the training data |
yhat Predicted value for each test point
predicted Sign of yhat, which is the final predicted outcome
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | library(kernlab)
library(caret)
data(benchmark.data)
example.data=benchmark.data[[1]]
training.samples=sample(1:dim(example.data)[1],floor(0.7*dim(example.data)[1]),replace=FALSE)
C=100
kernels=rep('radial',3)
degree=rep(0,3)
scale=rep(0,3)
sigma=c(0,2^seq(-3:0))
K=kernels.gen(example.data[,1:2], training.samples, kernels, degree, scale, sigma)
K.train=K$K.train
K.test=K$K.test
SEMKL.model=SEMKL.classification(K.train,example.data[training.samples,3], C)
predicted=prediction.Classification(SEMKL.model, K.test, example.data[training.samples,3])
confusionMatrix(factor(predicted$predict, levels=c(-1,1)),
factor(example.data[-training.samples,3],levels=c(-1,1)))
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