Description Usage Arguments See Also Examples
SVM.train
is a method training a Support Vector Machine with a linear kernel.
factors
specifies whether linear weights are used (1
) or not (0
).
If linear weights are not used intercept
is set to TRUE
.
1 2 |
data |
an object of class |
target |
|
factors |
either |
intercept |
|
iter |
|
regular |
|
stdev |
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ## Not run:
### Example to illustrate the usage of the method
### Data set very small and not sparse, results not representative
### Please study major example in general help 'FactoRizationMachines'
# Load data set
library(FactoRizationMachines)
library(MASS)
data("Boston")
# Subset data to training and test data
set.seed(123)
subset=sample.int(nrow(Boston),nrow(trees)*.8)
data.train=Boston[subset,-ncol(Boston)]
target.train=Boston[subset,ncol(Boston)]
data.test=Boston[-subset,-ncol(Boston)]
target.test=Boston[-subset,ncol(Boston)]
# Predict with linear weights and intercept with MCMC regularization
model=SVM.train(data.train,target.train)
# RMSE resulting from test data prediction
sqrt(mean((predict(model,data.test)-target.test)^2))
# Predict with linear weights but without intercept with MCMC regularization
model=SVM.train(data.train,target.train,intercept=FALSE)
# RMSE resulting from test data prediction
sqrt(mean((predict(model,data.test)-target.test)^2))
# Predict with linear weights and manual regularization
model=SVM.train(data.train,target.train,regular=0.1)
# RMSE resulting from test data prediction
sqrt(mean((predict(model,data.test)-target.test)^2))
## End(Not run)
|
[1] 13.165
[1] 8.836472
[1] 8.807126
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