Cluster evaluation metrics in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that calculates the silhouette score.

ML Python

from sklearn.metrics import [1]

score = [1](X, labels)

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Asilhouette_score

Bmean_squared_error

Caccuracy_score

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score which is for classification.

Using mean_squared_error which is for regression.

The silhouette_score function measures how well samples are clustered.

2fill in blank

medium

Complete the code to calculate the Davies-Bouldin index for clustering evaluation.

ML Python

from sklearn.metrics import davies_bouldin_score

score = davies_bouldin_score(X, [1])

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Apredictions

Blabels

Cfeatures

Dtargets

Attempts:

3 left

💡 Hint

Common Mistakes

Passing features or targets instead of cluster labels.

Using predictions which is ambiguous here.

The Davies-Bouldin index requires the cluster labels to evaluate clustering quality.

3fill in blank

hard

Fix the error in the code to compute the adjusted Rand index correctly.

ML Python

from sklearn.metrics import adjusted_rand_score

score = adjusted_rand_score([1], true_labels)

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Alabels

BX

Cfeatures

Dpredicted_labels

Attempts:

3 left

💡 Hint

Common Mistakes

Passing feature data instead of predicted labels.

Using generic variable names that don't represent predicted clusters.

The adjusted Rand index compares two cluster labelings: predicted and true labels.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each cluster label to its count.

ML Python

cluster_counts = {label: [1] for label in set(labels) if [2]

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Alabels.count(label)

Blabel > 0

Clabel >= 0

Dlen(labels)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(labels) inside the comprehension which is unrelated.

Filtering labels with label > 0 which excludes zero.

The comprehension counts how many times each label appears. We include labels that are zero or positive.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each cluster label to the average silhouette score of its points.

ML Python

silhouette_avg = {label: sum(scores[i] for i, l in enumerate(labels) if l == [1]) / [2] for label in set(labels) if [3]

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Alabel

Bsum(1 for i, l in enumerate(labels) if l == label)

Clabel >= 0

Dlen(scores)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(scores) as denominator which is total points, not per cluster.

Filtering labels with label > 0 which excludes zero.

This comprehension calculates the average silhouette score per cluster label, including only labels zero or above.