Final answer:
The k-NN algorithm can be used for both classification and regression tasks. It classifies by majority vote among the k closest data points, and it predicts numeric values by averaging the values of those neighbors. Therefore, the correct answer is that the k-NN algorithm can be used for both classification and regression.
Step-by-step explanation:
The k-NN algorithm (k-nearest neighbors algorithm) is a versatile machine-learning method that can be used for both classification and regression tasks. In classification, it identifies the class of an observation by finding the most common class among its k closest examples in the data set. In regression, it predicts a numeric value based on the average of the k closest examples.
To clarify, option a) states that it can be used for classification, which is true, as k-NN can classify items by majority vote of their neighbors. Option b) claims that it can be used for regression, which is also true, as k-NN can predict a continuous value by averaging the values of the neighbors. Consequently, option c) which can be used for both classification and regression is the correct answer because k-NN is adaptive to both types of problems, depending on the nature of the output variable (categorical for classification, continuous for regression).