How do I choose the right k value for my KNN model?
An example of an AI chatbot conversation about selecting the optimal k value in KNN.
How do I choose the right k value for my KNN model?
Choosing the right k value is crucial for KNN performance. A common approach is to use cross-validation.
What happens if k is too small or too large?
If k is too small, the model may be sensitive to noise. If k is too large, it may oversimplify the decision boundary.
Is there a rule of thumb for selecting k?
A common rule of thumb is to set k as the square root of the number of data points in your training set.
Can I use grid search to find the best k?
Yes, grid search is an effective method to test various k values and find the one that yields the best performance.
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