3. How can we gauge the performance of a k-means clustering model when ground truth is not avaliable? Take the average of the distance between data points and their cluster centroids. Calculate the R-squared value to measure model fit. Determine the prediction accuracy on the test set. Calculate the number of incorrectly classified observations in the training set. 4. When the parameter K for k-means clustering increases, what happens to the error? It will decrease because the data points are less possible to be in the wrong cluster. It will decrease because distance between data points and centroid will decrease. It will increase because incorrectly classified points are further from the correct centroid. It might increase or decrease depending on if data points are closer to the centroid