You have built an SVM for a binary classification problem but the model is overfitting. What changes can you make to the kernel or hyperparameters to improve the model?
- Change the kernel's color
- Change to a simpler kernel or adjust the regularization parameter 'C'
- Ignore overfitting
- Increase the kernel's complexity
Overfitting can be mitigated by choosing a simpler kernel or adjusting the regularization parameter 'C', allowing for a better balance between bias and variance.
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