MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Minkowski Distance is the generalized form of Euclidean and Manhattan Distance .

Detailed explanation-2: -Both Euclidean and Manhattan distances are used in case of continuous variables, whereas hamming distance is used in case of categorical variable.

Detailed explanation-3: -Euclidean Distance – This distance is the most widely used one as it is the default metric that SKlearn library of Python uses for K-Nearest Neighbour. It is a measure of the true straight line distance between two points in Euclidean space.

Detailed explanation-4: -Minkowski distance is a distance measured between two points in N-dimensional space. It is basically a generalization of the Euclidean distance and the Manhattan distance. It is widely used in the field of Machine learning, especially in the concept to find the optimal correlation or classification of data.

Detailed explanation-5: -ED is the most widely used distance metric in KNN classifications; however, only few studies examined the effect of different distance metrics on the performance of KNN, these used a small number of distances, a small number of data sets, or both.