For a given set of training data and a query xq, the k-nearest-neighbours algorithm finds the k examples that are nearest to xq based on a selected distance measure, denoted as NN(k, xq). Suppose we use KNN to solve a regression problem  by taking the average of the output values of k neighbours. If the output values of k=4 neighbours are {8,9,11,12), what is the output for this query sample xq (Keep one decimal place)?

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

Which of the statement is INCORRECT about KNN algorithm? KNN works ONLY for binary classification problems If k=1, then the algorithm is simply called the nearest neighbour algorithm Number of neighbours (K) will influence classification output None of the above

When applying k-Nearest Neighbors (KNN) for classification, what is the role of the "k" parameter?a.It determines the learning rate in the algorithm.b.It sets the number of nearest neighbors to consider for classification.c.It specifies the number of dimensions in the dataset.d.It defines the number of clusters.

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.