ML week 5

1.

Question 1

The objective of k-means clustering is:

1 point

Separate dissimilar samples and group similar ones

Maximize the number of correctly classified data points

Minimize the cost function via gradient descent

Yield the highest out of sample accuracy

2.

Question 2

Which option correctly orders the steps of k-means clustering?

1. Re-cluster the data points

2. Choose k random observations to calculate each cluster’s mean

3. Update centroid to take cluster mean

4. Repeat until centroids are constant

5. Calculate data point distance to centroids

1 point

3, 5, 1, 4, 2

2, 3, 4, 5, 1

2, 5, 3, 1, 4

2, 1, 4, 5, 3

3.

Question 3

How can we gauge the performance of a k-means clustering model when ground truth is not available?

1 point

Calculate the R-squared value to measure model fit.

Take the average of the distance between data points and their cluster centroids.

Calculate the number of incorrectly classified observations in the training set.

Determine the prediction accuracy on the test set.

4.

Question 4

When the parameter K for k-means clustering increases, what happens to the error?

1 point

It might increase or decrease depending on if data points are closer to the centroid.

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 will decrease because the data points are less possible to be in the wrong cluster.

5.

Question 5

Which of the following is true for partition-based clustering but not hierarchical nor density-based clustering algorithms?

1 point

Partition-based clustering produces arbitrary shaped clusters.

Partition-based clustering can handle spatial clusters and noisy data.

Partition-based clustering produces sphere-like clusters.

Partition-based clustering is a type of unsupervised learning algorithm.