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)

Drag options to blanks, or click blank then click option'

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])

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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]

Drag options to blanks, or click blank then click option'

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.

Practice

1. Which of the following cluster evaluation metrics requires knowing the true labels of the data?

easy

A. Davies-Bouldin Index

B. Silhouette Score

C. Adjusted Rand Index (ARI)

D. Calinski-Harabasz Index

Solution

1. Step 1: Understand metric types

   Some cluster metrics need true labels (external metrics), others only use cluster assignments (internal metrics).

2. Step 2: Identify ARI as external metric

   Adjusted Rand Index compares predicted clusters to true labels, so it requires true labels.

3. Final Answer:

   Adjusted Rand Index (ARI) -> Option C

4. Quick Check:

   External metric = ARI [OK]

Hint: Only ARI needs true labels; others use cluster data alone [OK]

Common Mistakes:

• Confusing Silhouette Score as needing true labels
• Thinking Davies-Bouldin Index requires true labels
• Assuming Calinski-Harabasz Index uses true labels

2. Which of the following is the correct way to compute the Silhouette Score in Python using scikit-learn for data X and cluster labels labels?

easy

A. from sklearn.metrics import silhouette_score
score = silhouette_score(X, labels)

B. from sklearn.cluster import silhouette_score
score = silhouette_score(labels, X)

C. from sklearn.metrics import silhouette_score
score = silhouette_score(labels, X)

D. from sklearn.metrics import silhouette_score
score = silhouette_score(X)

Solution

1. Step 1: Check import source

   Silhouette Score is in sklearn.metrics, not sklearn.cluster.

2. Step 2: Check function parameters

   Function signature is silhouette_score(X, labels), where X is data and labels are cluster assignments.

3. Final Answer:

   from sklearn.metrics import silhouette_score\nscore = silhouette_score(X, labels) -> Option A

4. Quick Check:

   Correct import and parameter order = D [OK]

Hint: Import from metrics and pass data first, labels second [OK]

Common Mistakes:

• Importing silhouette_score from sklearn.cluster
• Swapping data and labels in function call
• Calling silhouette_score with only data

3. Given the following code, what will be the output of the Davies-Bouldin Index?

from sklearn.metrics import davies_bouldin_score
X = [[1, 2], [2, 1], [10, 10], [11, 11]]
labels = [0, 0, 1, 1]
score = davies_bouldin_score(X, labels)
print(round(score, 2))

medium

A. 0.50

B. 1.41

C. 1.00

D. 0.11

Solution

1. Step 1: Understand Davies-Bouldin Index meaning

   Lower values mean better clusters; it measures average similarity between clusters.

2. Step 2: Calculate score using sklearn

   Running the code gives approximately 0.1111, rounded to 0.11.

3. Final Answer:

   0.11 -> Option D

4. Quick Check:

   Davies-Bouldin score ≈ 0.11 [OK]

Hint: Run sklearn function and round result to 2 decimals [OK]

Common Mistakes:

• Confusing Davies-Bouldin with Silhouette Score values
• Rounding incorrectly
• Misinterpreting higher score as better

4. The following code throws an error. What is the most likely cause?

from sklearn.metrics import silhouette_score
X = [[1, 2], [2, 1], [10, 10], [11, 11]]
labels = [0, 0, 1]
score = silhouette_score(X, labels)
print(score)

medium

A. Mismatch in length between X and labels

B. silhouette_score requires true labels, not cluster labels

C. X should be a numpy array, not a list

D. silhouette_score cannot handle more than 3 clusters

Solution

1. Step 1: Check input lengths

   Data X has 4 samples, but labels list has only 3 elements, causing mismatch error.

2. Step 2: Understand silhouette_score input requirements

   silhouette_score requires labels length equal to number of samples in X.

3. Final Answer:

   Mismatch in length between X and labels -> Option A

4. Quick Check:

   Length mismatch error = A [OK]

Hint: Ensure labels length matches data samples count [OK]

Common Mistakes:

• Thinking silhouette_score needs true labels
• Assuming lists instead of arrays cause error
• Believing cluster count limits cause error

5. You have clustered customer data into 3 groups but want to evaluate cluster quality without true labels. Which combination of metrics gives the best overall insight?

hard

A. Adjusted Rand Index and Calinski-Harabasz Index

B. Silhouette Score and Davies-Bouldin Index

C. Homogeneity Score and Completeness Score

D. Adjusted Mutual Information and Silhouette Score

Solution

1. Step 1: Identify metrics that do not require true labels

   Silhouette Score and Davies-Bouldin Index are internal metrics needing only data and cluster labels.

2. Step 2: Understand other metrics need true labels

   Adjusted Rand Index, Homogeneity, Completeness, and Adjusted Mutual Information require true labels, which are unavailable.

3. Final Answer:

   Silhouette Score and Davies-Bouldin Index -> Option B

4. Quick Check:

   Internal metrics only = A [OK]

Hint: Use only internal metrics when true labels are missing [OK]

Common Mistakes:

• Choosing metrics that require true labels
• Using only one metric instead of combination
• Confusing internal and external metrics