Hierarchical clustering in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the correct function for hierarchical clustering.

ML Python

from scipy.cluster.hierarchy import [1]

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Afit_predict

Btrain_test_split

Clinkage

Dkmeans

Attempts:

3 left

2fill in blank

medium

Complete the code to compute the linkage matrix using the 'ward' method.

ML Python

Z = linkage(data, method=[1])

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A'average'

B'ward'

C'single'

D'complete'

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to plot a dendrogram from the linkage matrix.

ML Python

dendrogram([1])
plt.show()

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Alinkage

Bdata

Cplt

DZ

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary of cluster labels for each sample using fcluster with a distance threshold.

ML Python

from scipy.cluster.hierarchy import fcluster
labels = fcluster([1], t=[2], criterion='distance')

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AZ

Bdata

C5

D10

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to compute the linkage matrix, plot the dendrogram, and assign cluster labels with max clusters = 3.

ML Python

Z = [1](data, method='ward')
dendrogram([2])
labels = fcluster(Z, t=[3], criterion='maxclust')

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Alinkage

BZ

C3

Dfcluster

Attempts:

3 left