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

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 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

What is the K in K-Nearest Neighbors?Answer areaThe number of classesThe number of nearest neighbors to consider for classificationThe number of features in the datasetThe number of layers in the model

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 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.