Description Usage Arguments Value Examples
View source: R/classification.R
SVM
1 2 3 4 5 6 7 8 |
data_train |
Training set: dataframe containing classification column and all other columns features. This is the dataset on which the decision tree model is trained. |
data_test |
Test set: dataframe containing classification column and all other columns features. This is the dataset on which the decision tree model in tested. |
kernel |
Type of kernel to use for SVM model (default:linear) |
degree |
Degree for kernel used (in polynomial or radial case) |
poly |
Binary parameter stating whether the chosen kernel is polynomial of degree greater than 1 (default:0) |
includeplot |
Show performance scatter plot (default:FALSE) |
List containing performance percentages, accessed using training (training accuracy), test (test accuracy), trainsensitivity, testsensitivity, trainspecificity, testspecificity.
1 2 3 4 5 6 7 8 9 10 11 12 13 | data_train = data.frame(
classification=as.factor(c(1,1,0,0,1,1,0,0,1,1)),
A=c(1,1,1,0,0,0,1,1,1,0),
B=c(0,1,1,0,1,1,0,1,1,0),
C=c(0,0,1,0,0,1,0,0,1,0))
data_test = data.frame(
classification=as.factor(c(1,1,0,0,1,1,1,0)),
A=c(0,0,0,1,0,0,0,1),
B=c(1,1,1,0,0,1,1,1),
C=c(0,0,1,1,0,0,1,1))
svm(data_train,data_test,kernel='radial',degree=3)
svm(data_train,data_test,kernel='sigmoid')
svm(data_train,data_test,kernel='poly',degree=4,poly=1)
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