Why is the k-nearest neighbors regression called a lazy learner?

a. Because k-nearest neighbors does not have embedded feature selection.

b. Because the k-nearest neighbors regression requires the standardization or normalization of the variables.

c. Because the k-nearest neighbors regression does not use training data to estimate model parameters or determine variable splitting rules, but instead directly uses the average of the k most similar training set observations as its predicted value for a new observation.

d. Because the k-nearest neighbors method does not require the use of a test set.