How to call cv.svm in r language

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When using R to tune the RBF kernel in the SVR model, it was found that the "cv.svm" function could not be found, which was indeed defined in the e1071 package. This may occur because your e1071 package version is older and does not contain the latest cv.svm functions.

The solution to this problem is to upgrade your e1071 package version. In R, you can install the latest version of the e1071 package with the following code:

install.packages("e1071")

If you already have package e1071 installed, you can try upgrading to the latest version:

update.packages("e1071")

If you have tried to upgrade the e1071 package and the problem has not been resolved, try using another package for cross-validation. For example, the caret package provides a set of functions for cross-validation, including the train and trainControl functions. The train function can be used to train the model, and the trainControl function can be used to specify parameters for cross validation. For example, the following code shows how to cross-validate SVR models using the caret package:

library(caret)
# 创建交叉验证参数
ctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
# 训练模型
model <- train(y ~ ., data = train, method = "svmRadial", tuneGrid = expand.grid(sigma = gamma_range, C = cost_range), trControl = ctrl)

In the above code, the trainControl function specifies 10 fold cross validation, which is repeated 3 times for each parameter combination. The train function uses the "svmRadial" method to train the model and specifies the scope of the parameters through the tuneGrid parameter. Finally, the performance of the model can be viewed using model$results.