1. The plot below displays a set of 2D data points that are uniformly distributed. Suppose you want to use K-Means to cluster these points into three groups. Please select  the final cluster boundaries that you anticipate K-Means may produce.

(i) The time of day displayed on a 24-hour clock (e.g., 13:00, 15:30,17:59) 

(a) In K-Means, changing the initial centroids can lead to different final clusters. 

(h)  The altitude above sea level recorded in meters.

Extra Credit: Which of the following are the indicators of overfitting in a model? (There can be more than two answers)

(j)  K-Means is ideal for detecting arbitrarily shaped clusters.

Extra Credit: If overfitting is observed in a model, which of the following changes would be most beneficial? (There can be more than two answers)

Does this curve indicate overfitting? If so, where does overfitting occur? Select one of the options below.

(c) The KNN algorithm relies on feature scaling (e.g., normalization) to work effectively when using Euclidean distance as a metric.

(i)  Support Vector Machine can handle high dimensional data.