What could be the possible consequence of choosing a very small value of K in the KNN algorithm?
- Increased efficiency
- Overfitting
- Reduced complexity
- Underfitting
Choosing a very small value of K in the KNN algorithm can lead to overfitting, where the model becomes too sensitive to noise in the training data.
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