How do I tune the parameters of an SVM model?
An example of an AI chatbot conversation about tuning SVM parameters for optimal performance.
How do I tune the parameters of an SVM model?
Tuning SVM parameters involves adjusting the kernel type, C, and gamma values. Would you like to know more about each?
Yes, can you explain the role of the C parameter?
The C parameter controls the trade-off between achieving a low training error and a low testing error. A smaller C encourages a smoother decision boundary, while a larger C aims for a more complex boundary.
What about the gamma parameter?
Gamma defines how far the influence of a single training example reaches. A low gamma means 'far' and a high gamma means 'close'. It can significantly affect the model's performance.
How can I find the best values for C and gamma?
You can use techniques like Grid Search or Random Search with cross-validation to find the optimal values for C and gamma.
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