Check your understanding of this module before moving on.
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Checkpoint quiz 3 questions
A quick check on this module's key ideas before you move on
Question 01
What does the K in K-means clustering refer to?
B is correct. K is a hyperparameter you choose up front: how many clusters K-means should partition the data into.
Question 02
K-means assigns each point to the cluster with the nearest centroid, then updates each centroid to the mean of its assigned points, and repeats. Why does this process eventually stop changing?
B is correct. Both the assignment step and the centroid-update step never increase the total within-cluster squared distance, so the algorithm is guaranteed to settle into a stable (if not always globally optimal) arrangement.
Question 03
What is the main goal of Principal Component Analysis (PCA)?
B is correct. PCA finds directions of maximum variance in the data and re-expresses it along those directions, letting you keep only the top few components while preserving as much of the original signal as possible.