DBSCAN clustering in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding DBSCAN Core Concepts

Which statement best describes the role of the epsilon (eps) parameter in DBSCAN clustering?

A It sets the minimum number of clusters to find in the data.

B It defines the maximum distance between two points to be considered neighbors.

C It controls the maximum number of iterations during clustering.

D It specifies the initial number of centroids for clustering.

💡 Hint

Think about how DBSCAN decides if points belong to the same cluster based on distance.

The epsilon (eps) parameter sets the radius to search for neighboring points. Points within this radius are considered neighbors, which helps define clusters.

❓ Predict Output

intermediate

DBSCAN Cluster Labels Output

What is the output of the following code snippet?

ML Python

from sklearn.cluster import DBSCAN
import numpy as np

X = np.array([[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]])
model = DBSCAN(eps=1.5, min_samples=2)
labels = model.fit_predict(X)
print(labels.tolist())

A [0, 0, 0, 1, 1, -1]

B [0, 0, 0, 1, 1, 1]

C [0, 0, 1, 1, 1, -1]

D [-1, -1, -1, 0, 0, 1]

💡 Hint

Check which points are close enough to form clusters with eps=1.5 and min_samples=2.

The first three points form one cluster (label 0), the next two form another cluster (label 1), and the last point is an outlier (-1) because it has no neighbors within eps.

❓ Hyperparameter

advanced

Effect of Changing min_samples in DBSCAN

What is the most likely effect of increasing the min_samples parameter in DBSCAN while keeping eps constant?

A The eps parameter will automatically adjust to compensate.

B Clusters will merge into fewer larger clusters.

C The algorithm will run faster due to fewer clusters.

D More points will be labeled as noise because clusters require more neighbors.

💡 Hint

Think about how min_samples controls the minimum number of points to form a cluster.

Increasing min_samples means a point needs more neighbors to be a core point, so fewer points meet this, increasing noise points.

❓ Metrics

advanced

Evaluating DBSCAN Clustering Quality

Which metric is most appropriate to evaluate the quality of clusters produced by DBSCAN when true labels are unknown?

A Cross-Entropy Loss

B Accuracy

C Silhouette Score

D Mean Squared Error

💡 Hint

Consider metrics that do not require true labels and measure cluster cohesion and separation.

Silhouette Score measures how similar points are within clusters compared to other clusters and works without true labels.

🔧 Debug

expert

Identifying the Cause of Unexpected DBSCAN Output

Given the code below, why does the DBSCAN model assign all points to noise (-1)?

ML Python

from sklearn.cluster import DBSCAN
import numpy as np

X = np.array([[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]])
model = DBSCAN(eps=0.5, min_samples=2)
labels = model.fit_predict(X)
print(labels.tolist())

A The eps value is too small, so no points have enough neighbors to form clusters.

B The min_samples value is too low, causing all points to be noise.

C The input data must be normalized before applying DBSCAN.

D DBSCAN requires at least 10 points to form clusters.

💡 Hint

Check the distance threshold and how it affects neighbor counts.

With eps=0.5, points are too far apart to have neighbors within that radius, so none become core points and all are labeled noise.

Practice

1. What is the main advantage of using DBSCAN clustering compared to K-Means?

easy

A. It does not require specifying the number of clusters beforehand.

B. It always produces clusters of equal size.

C. It only works with numerical data.

D. It requires labeled data to work.

Solution

1. Step 1: Understand DBSCAN's clustering approach

   DBSCAN groups points based on density and does not need the number of clusters as input.

2. Step 2: Compare with K-Means

   K-Means requires the number of clusters to be specified before running the algorithm.

3. Final Answer:

   It does not require specifying the number of clusters beforehand. -> Option A

4. Quick Check:

   DBSCAN = no cluster count needed [OK]

Hint: DBSCAN finds clusters by density, no cluster count needed [OK]

Common Mistakes:

• Thinking DBSCAN needs number of clusters like K-Means
• Assuming DBSCAN only works with numerical data
• Believing DBSCAN requires labeled data

2. Which of the following is the correct way to import and create a DBSCAN model in Python using scikit-learn?

easy

A. import DBSCAN from sklearn\ndbscan = DBSCAN(0.5, 5)

B. import dbscan from sklearn.cluster\ndbscan = dbscan(eps=0.5, min_samples=5)

C. from sklearn.cluster import dbscan\ndbscan = dbscan(eps=0.5, min_samples=5)

D. from sklearn.cluster import DBSCAN\ndbscan = DBSCAN(eps=0.5, min_samples=5)

Solution

1. Step 1: Check correct import syntax

   The correct import is from sklearn.cluster import DBSCAN with capital letters.

2. Step 2: Check model creation syntax

   DBSCAN is created by calling DBSCAN(eps=0.5, min_samples=5) with named parameters.

3. Final Answer:

   from sklearn.cluster import DBSCAN\ndbscan = DBSCAN(eps=0.5, min_samples=5) -> Option D

4. Quick Check:

   Correct import and parameters = from sklearn.cluster import DBSCAN\ndbscan = DBSCAN(eps=0.5, min_samples=5) [OK]

Hint: Use capital DBSCAN and named parameters eps, min_samples [OK]

Common Mistakes:

• Using lowercase dbscan instead of DBSCAN
• Wrong import syntax like import dbscan from sklearn.cluster
• Passing parameters without names or in wrong order

3. Given the following code, what will be the output of print(labels)?

from sklearn.cluster import DBSCAN
import numpy as np

X = np.array([[1, 2], [2, 2], [8, 7], [8, 8], [25, 80]])
dbscan = DBSCAN(eps=3, min_samples=2)
dbscan.fit(X)
labels = dbscan.labels_
print(labels)

medium

A. [0 0 1 1 -1]

B. [0 1 0 1 -1]

C. [1 1 0 0 -1]

D. [-1 -1 -1 -1 -1]

Solution

1. Step 1: Understand DBSCAN parameters and data points

   eps=3 means points within distance 3 are neighbors; min_samples=2 means at least 2 points to form a cluster. Points [1,2] and [2,2] are close, so cluster 0. Points [8,7] and [8,8] are close, so cluster 1. Point [25,80] is far and isolated, so noise (-1).

2. Step 2: Assign labels based on clusters and noise

   First cluster points get label 0, second cluster 1, noise points get -1.

3. Final Answer:

   [0 0 1 1 -1] -> Option A

4. Quick Check:

   Clusters and noise labels = [0 0 1 1 -1] [OK]

Hint: Close points cluster, isolated points labeled -1 noise [OK]

Common Mistakes:

• Mixing cluster labels order
• Forgetting noise points get label -1
• Assuming all points form one cluster

4. You run DBSCAN with eps=0.5 and min_samples=3 on a dataset but get all points labeled as -1. What is the most likely cause?

medium

A. The dataset has too many clusters.

B. The eps value is too small, so no points have enough neighbors.

C. The min_samples value is too low, causing all points to be noise.

D. DBSCAN requires the number of clusters to be set manually.

Solution

1. Step 1: Analyze effect of eps and min_samples

   eps controls neighborhood radius; if too small, points have few neighbors. min_samples=3 means at least 3 points needed to form a cluster.

2. Step 2: Reason why all points are noise

   If eps is too small, no point has 3 neighbors within that radius, so all points become noise (-1).

3. Final Answer:

   The eps value is too small, so no points have enough neighbors. -> Option B

4. Quick Check:

   Small eps causes all noise = The eps value is too small, so no points have enough neighbors. [OK]

Hint: Too small eps means no clusters, all points noise [OK]

Common Mistakes:

• Thinking min_samples too low causes noise
• Believing DBSCAN needs cluster count input
• Assuming many clusters cause noise labels

5. You have a dataset with two dense clusters and some scattered noise points. You want DBSCAN to find the clusters and mark noise correctly. Which combination of eps and min_samples is best to try first?

hard

A. eps=0.3, min_samples=10

B. eps=5, min_samples=2

C. eps=1, min_samples=5

D. eps=0.1, min_samples=1

Solution

1. Step 1: Understand parameter effects

   eps controls neighborhood size; min_samples controls minimum points to form cluster. Too small eps or too high min_samples may miss clusters; too large eps or too low min_samples may merge clusters or label noise incorrectly.

2. Step 2: Choose balanced parameters

   eps=1 and min_samples=5 is a common starting point balancing cluster detection and noise filtering for dense clusters.

3. Final Answer:

   eps=1, min_samples=5 -> Option C

4. Quick Check:

   Balanced eps and min_samples = eps=1, min_samples=5 [OK]

Hint: Start with moderate eps=1 and min_samples=5 for dense clusters [OK]

Common Mistakes:

• Using too small eps causing all noise
• Using too low min_samples merging noise as clusters
• Choosing very large eps merging clusters incorrectly