#--------------------------------------------------------------------
# SVM
#--------------------------------------------------------------------
formula <- as.formula(factor(DIABETE2) ~ . );
svm.tune <- tune.svm(formula, data = train.set,
gamma = 10^(-3:0), cost = 10^(-1:1));
svm.model <- svm(formula, data = train.set,
kernel = "linear",
gamma = svm.tune$best.parameters$gamma,
cost = svm.tune$best.parameters$cost);
#--------------------------------------------------------------------
# Confusion matrix
#--------------------------------------------------------------------
train.pred <- predict(svm.model, train.set);
test.pred <- predict(svm.model, test.set);
svm.table <- table(pred = test.pred, true = test.result);
print(svm.table); ·
http://stats.stackexchange.com/questions/14206/improving-the-svm-classification-of-diabetes#--------------------------------------------------------------------
# SVM
#--------------------------------------------------------------------
formula <- as.formula(factor(DIABETE2) ~ . );
svm.tune <- tune.svm(formula, data = train.set,
gamma = 10^(-3:0), cost = 10^(-1:1));
svm.model <- svm(formula, data = train.set,
kernel = "linear",
gamma = svm.tune$best.parameters$gamma,
cost = svm.tune$best.parameters$cost);
#--------------------------------------------------------------------
# Confusion matrix
#--------------------------------------------------------------------
train.pred <- predict(svm.model, train.set);
test.pred <- predict(svm.model, test.set);
svm.table <- table(pred = test.pred, true = test.result);
print(svm.table); ·
http://stats.stackexchange.com/questions/14206/improving-the-svm-classification-of-diabetes