Which distance metric is commonly used in k-Nearest Neighbors (KNN) for numerical data?a.Euclidean distanceb.Hamming distancec.Cosine similarityd.Jaccard distance

What is the most widely used distance metric in KNN?Euclidean distanceManhattan distancePerpendicular distanceAll of the aboveClear selection

Which one of the following statements is true regarding K Nearest Neighbors?1 pointFor high dimensional data, the best distance measure to use for KNN is the Euclidean distance.The Manhattan distance between two data points is the square root of the sum of the squares of the differences between the individual feature values of the data points.The distance between two data points is independent of the scale of their features.K Nearest Neighbors (KNN) assumes that points which are close together are similar.

Which of the following distance metric can be used in k-NN?Review LaterManhattanMinkowskiTanimotoJaccard

What parameter in KNN determines the distance metric used to find the nearest neighbors?Answer arean_neighborsmetricweightsalgorithm

Question 3Which one of the following statements is most accurate about K Nearest Neighbors (KNN)? 1 pointKNN is a classification model.KNN is an unsupervised learning method. KNN is a regression model.KNN can be used for both classification and regression.