Introduction

Ten-fold cross-validation is a standard way to estimate SVM performance without depending on a single train-test split. In R, the usual LibSVM interface is e1071::svm, which supports a quick built-in cross-validation mode and also allows manual fold loops when you need more control.

Quick Baseline With e1071::svm

The e1071 package is the most common LibSVM wrapper in R.

install.packages("e1071")
library(e1071)

A quick ten-fold run looks like this:

1set.seed(42)
2
3model_cv <- svm(
4  Species ~ ., 
5  data = iris,
6  kernel = "radial",
7  cost = 1,
8  gamma = 0.1,
9  cross = 10
10)
11
12print(model_cv$tot.accuracy)

This is convenient, but it only gives summary cross-validation accuracy. If you need per-fold predictions, custom metrics, or fold-specific logging, write the folds manually.

Manual Ten-Fold Cross-Validation

Manual folds give full control over data splitting, metrics, and diagnostics.

1library(e1071)
2
3set.seed(123)
4df <- iris
5k <- 10
6
7fold_id <- integer(nrow(df))
8for (cls in unique(df$Species)) {
9  idx <- which(df$Species == cls)
10  fold_id[idx] <- sample(rep(1:k, length.out = length(idx)))
11}
12
13fold_acc <- numeric(k)
14all_pred <- vector("list", k)
15
16for (fold in 1:k) {
17  train <- df[fold_id != fold, ]
18  test  <- df[fold_id == fold, ]
19
20  fit <- svm(Species ~ ., data = train, kernel = "radial", cost = 1, gamma = 0.1)
21  pred <- predict(fit, newdata = test)
22
23  fold_acc[fold] <- mean(pred == test$Species)
24  all_pred[[fold]] <- data.frame(actual = test$Species, pred = pred)
25}
26
27print(fold_acc)
28print(mean(fold_acc))
29print(sd(fold_acc))

This pattern is more verbose, but it is much better when you want to inspect variability across folds instead of relying on one summary number.

Why Stratification Matters

For classification, especially with imbalanced data, you should usually build stratified folds rather than random folds. The loop above assigns fold IDs within each class so each fold gets a more representative class distribution.

Without stratification, one fold may end up unusually easy or unusually hard, and the reported score becomes noisier than necessary.

Hyperparameter Tuning With Cross-Validation

SVM performance depends heavily on parameters such as cost and gamma. Use ten-fold cross-validation during tuning, not just after a single arbitrary model choice.

1set.seed(42)
2
3tuned <- tune.svm(
4  Species ~ ., 
5  data = iris,
6  gamma = 10^seq(-3, 0, by = 1),
7  cost  = 10^seq(-1, 2, by = 1),
8  tunecontrol = tune.control(cross = 10)
9)
10
11print(summary(tuned))
12print(tuned$best.parameters)

This helps you select a better model, but it introduces an important evaluation rule.

Do Not Report Tuning Performance as Final Test Performance

A common mistake is tuning hyperparameters with cross-validation and then reporting that same score as the final unbiased model result. A better workflow is:

1. split off a final holdout test set,
2. run cross-validation only on the training subset,
3. choose the best parameters,
4. retrain on the full training subset,
5. evaluate once on the holdout test set.

Example skeleton:

1set.seed(99)
2idx <- sample(seq_len(nrow(iris)), size = floor(0.8 * nrow(iris)))
3train <- iris[idx, ]
4test  <- iris[-idx, ]
5
6best <- tune.svm(
7  Species ~ ., data = train,
8  gamma = 10^seq(-3, 0, 1),
9  cost = 10^seq(-1, 2, 1),
10  tunecontrol = tune.control(cross = 10)
11)$best.model
12
13pred_test <- predict(best, test)
14mean(pred_test == test$Species)

That keeps model selection separate from final evaluation.

Prevent Data Leakage

If you standardize features, perform feature selection, or apply other preprocessing, fit those steps only on the training fold and then apply them to the test fold. If you preprocess the entire dataset before folding, the validation estimate becomes overly optimistic.

This matters even for small examples. Leakage is one of the fastest ways to get deceptively good cross-validation scores.

Common Pitfalls

A common mistake is creating folds without stratification for classification problems. That makes evaluation less stable.

Another issue is using cross-validation for parameter tuning and then treating the tuned CV score as the final test result.

Teams also often forget that preprocessing can leak information across folds if it is performed before the split.

Summary

• 'e1071::svm(cross = 10) is a quick way to run LibSVM-style ten-fold cross-validation in R.'
• Manual fold loops are better when you need custom metrics or per-fold diagnostics.
• Use stratified folds for classification problems.
• Tune cost and gamma with cross-validation, but keep final test evaluation separate.
• Avoid data leakage by fitting preprocessing steps only on training folds.