11. When creating a k-means model, what does it mean when an observation has a silhouette score coefficient with a value close to negative one?

  • The observation may be in the wrong cluster.
  • The observation is suitably within its own cluster and well separated from other clusters.
  • The observation is on the boundary between clusters.
  • The observation is in the correct cluster.

12. Which Python function fits a k-means model for multiple values of k by calculating the inertia for each value, appending it to a list, and returning that list?

  • silhouette score
  • cluster_image
  • k-means inertia
  • labels

13. Which of the following statements accurately describe the elbow method? Select all that apply.

  • When using the elbow method, data professionals aim to find the smoothest part of the curve.
  • The elbow method uses a line plot to visually compare the inertias of different models. 
  • There is not always an obvious elbow.
  • The sharpest bend in the curve is usually the model that will provide the most meaningful clustering of data.

14. A data professional is assigning each data point to its nearest centroid. Which step of the model-creation process are they working in?

  • Step one
  • Step three
  • Step four
  • Step two

15. Fill in the blank: In order to evaluate the _____ space in a k-means model, a data professional uses the inertia metric. This is the sum of the squared distances between each observation and its nearest centroid.

  • converged
  • midpoint
  • intracluster
  • intercluster

16. When creating a k-means model, what does it mean when an observation has a silhouette score coefficient with a value of zero?

  • The observation is in an appropriate cluster.
  • The observation may be in the wrong cluster.
  • The observation is suitably within its own cluster and well separated from other clusters.
  • The observation is on the boundary between clusters.

17. Which of the following statements correctly describe key aspects of k-means? Select all that apply.

  • The k-means clustering process has four steps that repeat until the model converges.
  • K-means organizes unlabeled data into clusters.
  • The position of the k-means centroid is the center of the cluster, also known as the mathematical mean.
  • K-means is a supervised partitioning algorithm.

18. Which of the following statements accurately describe the elbow method? Select all that apply.

  • There is always an obvious elbow.
  • The elbow method uses a line plot to visually compare the inertias of different models. 
  • When using the elbow method, data professionals find the sharpest bend in the curve.
  • The elbow method helps data professionals decide which clustering gives the most meaningful model.

19. A data professional is choosing the number of centroids to use in a k-means model and placing them in the data space. Which step of the model-creation process are they working in?

  • Step one
  • Step three
  • Step four
  • Step two

20. Fill in the blank: In order to evaluate the intracluster space in a k-means model, a data professional uses the _____ metric. This is the sum of the squared distances between each observation and its nearest centroid.

  • convergence
  • inertia
  • spread
  • silhouette score

21. Which metric would a data professional use to better understand the intracluster distance between data points and their centroids?

  • k-means inertia
  • silhouette score
  • cluster_image
  • labels
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