Intro to ML: Unsupervised Learning

Intro to ML: Unsupervised Learning

Assessment

Assessment

Created by

Josiah Wang

Mathematics, Computers, Fun

University

11 plays

Hard

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10 questions

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1.

Multiple Choice

1 min

1 pt

Does one expect two runs of k-means clustering to produce the same clustering results?

yes

no

Answer explanation

No, k-means is sensitive to the initialisation stage where centroids are randomly assigned to positions in the data space.

2.

Multiple Choice

1 min

1 pt

Is it possible that the assignment of observations to clusters doesn’t change between successive iterations in K-Means?

yes

no

can't say

Answer explanation

Yes! Each centroid is updated to the average position of the datapoints which were assigned to it in the previous iteration. If the previous update in centroid position did not result in new datapoints being assigned to it then it's position will not be updated.

3.

Multiple Choice

1 min

1 pt

True or False. The larger the number of centroids in K-means, the less likely the model is to overfit

True

False

Answer explanation

If you keep increasing the number of centroids, at some point K will equal the number of data points. This will result in each data instance being assigned its own unique cluster. You will be fitting the spurious noise, not the underling trend of the data! The challenge with k-means is picking the correct number of centroids for the problem.

4.

Multiple Choice

1 min

1 pt

True or False. The initial position of the clusters does not affect the final result of K-Means

True

False

Answer explanation

As the centroids are simply updated to the average position of the assigned clusters there is no guarantee of convergence on a global optimum. Rather convergence on local minima subject to cluster initialisation occurs.

5.

Multiple Choice

1 min

1 pt

A student has applied the k-means algorithm to an unsupervised problem. On analysis they find that the mean distance between data instances and the cluster centres which they are assigned is 0. What does this mean?

That the chosen value of k must equal the true number of clusters

That the chosen value of k must at least equal the number of datapoints

That this specific configuration (ie position) of k centroids is optimal for this dataset

None of these

Answer explanation

Assuming that there are no datapoints with identical attributes there will always be a positive mean distance between clusters and their assigned datapoints if a centroid has more than one data point assigned to it.

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