What happens to a k-NN model as you increase the value of k? (Select One correct answer)

Increasing the value of $k$ in a k-NN (k-Nearest Neighbors) model has specific effects on the model’s behavior and performance. Here’s a detailed breakdown of the effects:

1. Noise Reduction: A larger $k$ value helps to smooth out noise in the data. This can lead to more stable predictions, as the model considers more neighbors which can dilute the influence of any one outlier.

2. Bias-Variance Tradeoff: Increasing $k$ tends to increase bias and decrease variance. The model becomes less flexible, which means it may miss intricate patterns in the data (underfitting). As $k$ increases, the decision boundary becomes smoother and less complex.

3. Prediction Time: With a higher $k$, the computation can increase since the algorithm needs to examine more neighbors before making a prediction, though it typically remains efficient.

4. Choosing $k$: There is no one-size-fits-all for determining the best $k$; it usually requires cross-validation. Typically, a smaller $k$ results in a more complex model, while a larger $k$ can make the model more generalized.

In summary, as you increase $k$ in a k-NN model, the model's predictions become more robust to noise, but can lead to over-smoothing, increased bias, and potential underfitting.

The correct answer is: The model becomes less sensitive to noise and more generalized, but may underfit the training data.