How can you tune hyperparameters in SVM to prevent overfitting?
- Changing the color of hyperplane
- Increasing data size
- Reducing feature dimensions
- Using appropriate kernel and regularization
Tuning hyperparameters like the choice of kernel and regularization helps in controlling model complexity to prevent overfitting in SVM.
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