What is the output of the following code?

k-nearest neighbour techniques are computationally efficient in the prediction phase.

In a k-nearest neighbours classification setting, using k = n (where n is the number of observations in the data set), means that the majority class is predicted for every observation.

A drawback of Euclidean distance is that it is limited to two dimensions.

k-nearest neighbours with k = n (where n is the number of observations in the data set) yields more complex decision boundaries than 1-nearest neighbours.

What is the output of the following code?

What is the output of the following code?

What is the output of the following code?

What is the value of result in the following code?