Using the k-nearest neighbor machine learning algorithm for classification, larger values of k
A) sharpen the distinction between classes.
B) reduce the effect of noise on the classification.
C) increase the effect of noise on the classification.
D) do not change the effect of noise on the classification.

Question

Quizplus · Accepted Answer

Larger values of k in k-nearest neighbor algorithm smoothen the decision boundary by considering more neighboring data points for classification. This reduces the effect of noise and makes the model more robust. Therefore, Option B is correct. Conversely, smaller values of k can result in overfitting and may incorrectly classify the data points, which result in the model being more sensitive to noise. Option A is incorrect as larger values of k do not sharpen the distinction between classes, but they do help to generalize better. Option C and Option D are also incorrect as larger k values reduce the noise effect.

Using the K-Nearest Neighbor Machine Learning Algorithm for Classification, Larger