Which method is used to classify data points in k-Nearest Neighbors (k-NN)?Review LaterApplying a linear regression equationComputing the centroid of each clusterVoting based on the classes of nearest neighborsUsing the Gini impurity criterion

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.

What is the main goal of the k-nearest neighbors (k-NN) algorithm in data classification?To perform dimensionality reduction on the datasetTo classify data points based on the majority class among their k nearest neighborsTo generate association rules from transactional dataTo find the optimal number of clusters in the dataClear selection

In KNN, how is the class of a new data point determined in classification tasks?*1 pointMajority vote of its K nearest neighborsAverage of its K nearest neighbors' valuesWeighted vote of its K nearest neighborsRandom selection from its K nearest neighbors

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.