WorksheetsIntro to ML: Unsupervised Learning
Total questions: 10
Worksheet time: 9mins
Does one expect two runs of k-means clustering to produce the same clustering results?
yes
no
Is it possible that the assignment of observations to clusters doesn’t change between successive iterations in K-Means?
yes
no
can't say
True or False. The larger the number of centroids in K-means, the less likely the model is to overfit
True
False
True or False. The initial position of the clusters does not affect the final result of K-Means
True
False
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
The K-means algorithm was executed several times with different values of K. The mean distance between validation datapoints and the nearest centroid was calculated and plotted. From this plot determine the best value for K.
1
3
4
6
9
Which of the following are limitations of the k-means algorithm
It is sensitive to outliers
It is sensitive to initialisation
It has exponential time complexity with dataset size
It is not suitable for datasets containing non hyper-ellipsoids clusters
None of the above
What does GMM-EM optimise?
Minimises the average distance between the samples and the mean of the nearest Gaussian
Minimises the negative-log-likelihood of the model
Maximises the negative-log-likelihood of the mode
Maximises the classification rate
None of these
True or False. If the responsibility, rnk is high, it means that data point n is a plausible sample from the kth mixture
True
False
True or False? The only differences between GMM-EM and k-means is the non-isotropic distance to the centroids/means and that for GMM-EM this metric varies during the learning process.
True
False
