Given a 2-class dataset as shown in the picture below (red - positive class and blue - negative class), we apply kNN to perform the classification. Every time we take one sample as the testing point (i.e. the new point) and use the other 8 samples to classify this point; this is repeated 9 times, so each sample becomes the testing point once; based on the above, we can obtain an accuracy (or error rate).  Which of the following value of K will produce the lowest error?Group of answer choicesk = 3k = 2k = 1k = 5

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.

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

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.

Why is the KNN Algorithm known as Lazy Learner? How to find the best value for K in the KNN algorithm? Justify your(5+10=15 marks)answer

What is the output of your kNN classifier? Did you obtain a label for each pixel? How is that label computed?