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10 class o class 1 9 8 7 6 5 4 3 2 1 1 2 3 4 5 6 7 8 9 10 Figure 1: Data for Neareset Neighbour classification Consider k-Nearest Neighbour (kNN) classification on the dataset given in Fig. 1 using Euclidean distance. Recall, in kNN, we assign the label to a test point based on the majority class of k nearest training points. Note: The point can not be neighbour of itself. Consider all the given data points for training. In cases where two points have same distance, consider all the neighbours for majority voting even if the total number of neighbour exceed k. The ties are resolved in favor of class 1. 2.1 What is the training error of 1-NN and 2-NN classifiers? Explain your calculations. 10 class o class 1 9 8 7 6 5 4 3 2 1 1 2 3 4 5 6 7 8 9 10 Figure 1: Data for Neareset Neighbour classification Consider k-Nearest Neighbour (kNN) classification on the dataset given in Fig. 1 using Euclidean distance. Recall, in kNN, we assign the label to a test point based on the majority class of k nearest training points. Note: The point can not be neighbour of itself. Consider all the given data points for training. In cases where two points have same distance, consider all the neighbours for majority voting even if the total number of neighbour exceed k. The ties are resolved in favor of class 1. 2.1 What is the training error of 1-NN and 2-NN classifiers? Explain your calculations