Question 4(True/False) K Nearest Neighbors with large k tend to be the best classifiers.1 pointTrueFalse

Question 1(True/False) An advantage of k nearest neighbor methods is that they can leverage categorical data without encoding.1 pointTrueFalse

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.

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.

Which of the following statements about the K Nearest Neighbours and Gaussian Naive Bayes classifiers are true?Group of answer choicesThe complexity involved in running the KNN algorithm increases with more training samples because it will have to calculate more distances but the complexity of the Gaussian Naive Bayes algorithm does not.Both classifiers can be used on any given classification problem.When there are more than three features, we must use Gaussian Naive Bayes as KNN can only deal with three features given that Euclidean distance is only defined for up to three dimensions.The performance of both classifiers are expected to improve when the number of training samples increases

In K Nearest Neighbours classification, which of the following statements about K and N (the number of training samples) is/are true?Group of answer choicesK impacts both the accuracy and the computational complexity of the KNN algorithm.K and N should be linearly related, i.e. K/N should be a pre-determined constant.The time taken to perform the classification task increases with N.K should be odd to avoid ties.