Why advanced clustering finds complex structures in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the clustering algorithm.

ML Python

from sklearn.cluster import [1]

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AKMeans

Btrain_test_split

CLinearRegression

DPCA

Attempts:

3 left

💡 Hint

Common Mistakes

Importing train_test_split instead of a clustering algorithm.

Confusing clustering with regression or PCA.

The KMeans class is used for clustering data points into groups.

2fill in blank

medium

Complete the code to fit the clustering model on data X.

ML Python

model = KMeans(n_clusters=3)
model.[1](X)

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Ascore

Bpredict

Ctransform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict before fitting the model.

Using transform which is for dimensionality reduction.

The fit method trains the clustering model on the data.

3fill in blank

hard

Fix the error in the code to predict cluster labels.

ML Python

labels = model.[1](X)

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Apredict

Btransform

Cfit

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict to get labels.

Using transform which changes data representation.

The predict method assigns cluster labels to data points after the model is fitted.

4fill in blank

hard

Fill both blanks to create a dictionary of cluster sizes.

ML Python

cluster_sizes = {i: sum(labels [1] i) for i in range([2])}

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A==

B!=

C3

Dlen(labels)

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' instead of '==' causes wrong counts.

Using len(labels) instead of number of clusters in range.

We count how many labels equal each cluster index from 0 to 2 (3 clusters).

5fill in blank

hard

Fill all three blanks to filter data points in cluster 1 with feature > 5.

ML Python

filtered = [x for x, label in zip(X, labels) if label == [1] and x[[2]] [3] 5]

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A0

B1

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong cluster label number.

Using wrong feature index.

Using '<' instead of '>' for filtering.

We select points where cluster label is 1 and the first feature (index 0) is greater than 5.