Which of the following statements about k-NN is accurate?

The statement that k-NN can be computationally expensive with large datasets is accurate because, in k-NN (k-Nearest Neighbors), the algorithm relies on distance calculations between the query point and all points in the dataset to make predictions. As the dataset grows larger, the number of computations required increases significantly, which can lead to longer processing times and greater resource consumption. This characteristic makes k-NN less practical for large-scale datasets unless optimizations or approximations are implemented.

In contrast to this statement, the idea that k-NN is guaranteed to have the lowest error rate is misleading because the performance of k-NN heavily depends on various factors such as the choice of distance metric, the number of neighbors (k), and the distribution of the data.

The assertion that k-NN only works for small datasets is also not accurate, though it is more efficient with smaller datasets. It can be applied to larger datasets, but its computational expenses may hinder performance.

Finally, the claim that k-NN does not require any distance measure is incorrect. The effectiveness of k-NN is rooted in its reliance on a distance measure (like Euclidean distance or Manhattan distance) to ascertain which points are closest to a given query point. Without a distance measure