So here is the result of running the SVM algorithm:
library(e1071)
svm1 <- svm(Efficiency~., data=train,
method="C-classification", kernal="radial",
gamma=0.1, cost=10)
summary(svm1)
#--------
Call:
svm(formula = Efficiency ~ ., data = train, method = "C-classification",
kernal = "radial", gamma = 0.1, cost = 10)
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 10
gamma: 0.1
Number of Support Vectors: 61
( 39 21 1 )
Number of Classes: 3
Levels:
Bad Good Moderate
#------------
prediction <- predict(svm1, train)
xtab <- table(train$Efficiency, prediction)
xtab
#------------------
prediction
Bad Good Moderate
Bad 1 0 0
Good 0 48 0
Moderate 0 0 21