Consider a modified k-NN method in which once the k nearest neighbours to the query point are identified, you do a linear regression fit on them and output the fitted value for the query point. Which of the following is/are true regarding this method? (CO3)

Justify your answer.

(a) This method makes an assumption that the data is locally linear.

(b) In order to perform well, this method would need dense distributed training data.

(c) This method has higher bias compared to K-NN

(d) This method has higher variance compared to K-NN