Question 1

Figure from the original question paper You and your friend are building a KNN model to classify customers based on their shopping patterns. You use cross-validation to choose K = 6, while your friend guesses and sets K = 2. On the test set, you notice that your friend's model has a much higher training accuracy but much lower test accuracy than yours. Which of the following is the best explanation? You are running a K-means clustering algorithm on customer segmentation data. Each time you run the model, you get different cluster assignments, making it hard to compare results. Which parameter should you adjust in the kmeans() function to achieve more stable and consistent results?