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

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 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

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

Identify the difficulties with the k-nearest neighbor algorithm.

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.