When we classify an unseen instance (flower) by using the k-NN algorithm in the above dataset (i.e., binary classification), what might be the problem if we set the value of k to an even number, and what could be a possible solution?

TASK 2: The data for Task 2 is presented on the sheet “Iris” of the excel file “Coursework assignment two data.xlsx” You are trying to determine the type of Iris (versicolor or virginica). You have obtained a sample of Iris. This dataset includes the sepal length, sepal width, petal length, petal width, and the labels. Question 1. Use the K-nearest neighbour (k-NN) algorithm with the Euclidean distance measure to determine the type of the following observation (6.5, 3.0, 5.0, 1.4). Use relevant theories of the algorithm to justify your decision. (8 marks) 6 | Page Question 2. When we classify an unseen instance (flower) by using the k-NN algorithm in the above dataset (i.e., binary classification), what might be the problem if we set the value of k to an even number, and what could be a possible solution? (4 marks) Question 3. Apply 3-NN, 5-NN, 7-NN, and 9-NN models, calculate the confusion matrix, and evaluate the models. Which model do you think is the best? Explain your choice. (14 marks) Question 4. Are you aware of other classification models/algorithms that can be used to decide the type of Iris? Propose two different types of models / algorithms and explain briefly how you plan to use them. (14 marks)