Mean shift clustering in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the MeanShift class from scikit-learn.

ML Python

from sklearn.cluster import [1]

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AMeanShift

BKMeans

CDBSCAN

DAgglomerativeClustering

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a different clustering class like KMeans or DBSCAN.

Misspelling the class name.

The MeanShift class is imported from sklearn.cluster to perform mean shift clustering.

2fill in blank

medium

Complete the code to create a MeanShift object with default bandwidth.

ML Python

ms = MeanShift([1]=None)

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Amax_iter

Brandom_state

Cn_clusters

Dbandwidth

Attempts:

3 left

💡 Hint

Common Mistakes

Using max_iter or n_clusters which are not parameters for bandwidth.

Setting random_state which is unrelated here.

The bandwidth parameter controls the window size for mean shift. Setting it to None lets the algorithm estimate it automatically.

3fill in blank

hard

Fix the error in the code to fit the MeanShift model on data X.

ML Python

ms = MeanShift()
ms.[1](X)

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Apredict

Bfit

Ctransform

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict before fitting the model.

Using transform which is not available for MeanShift.

The fit() method trains the MeanShift model on the data X.

4fill in blank

hard

Fill both blanks to get cluster labels and cluster centers from the fitted model.

ML Python

labels = ms.[1]
centers = ms.[2]

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Alabels_

Bcluster_centers_

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using methods like fit or predict instead of attributes.

Confusing labels_ with cluster_centers_.

After fitting, labels_ gives the cluster labels and cluster_centers_ gives the cluster centers.

5fill in blank

hard

Fill all three blanks to compute the number of clusters and print it.

ML Python

n_clusters = len(set(ms.[1]))
print('Number of clusters:', [2])
print('Labels:', ms.[3])

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Alabels_

Bn_clusters

Dcluster_centers_

Attempts:

3 left

💡 Hint

Common Mistakes

Using cluster_centers_ instead of labels_ to count clusters.

Printing wrong variables or attributes.

We use labels_ to find unique clusters with set(), count them with len(), store in n_clusters, then print n_clusters and labels_.