K-Means clustering in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the K-Means Objective

What is the main goal of the K-Means clustering algorithm?

A To minimize the sum of squared distances between data points and their assigned cluster centers

B To find the cluster centers that maximize the total variance within clusters

C To assign each data point to a unique cluster without overlap

D To maximize the distance between all data points in the dataset

❓ Predict Output

intermediate

Output of K-Means Cluster Assignments

What is the output of the following Python code snippet using scikit-learn's KMeans?

ML Python

from sklearn.cluster import KMeans
import numpy as np

X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
print(kmeans.labels_)

A [1 1 1 0 0 0]

B [0 1 0 1 0 1]

C [1 0 1 0 1 0]

D [0 0 0 1 1 1]

❓ Hyperparameter

advanced

Choosing the Number of Clusters (k)

Which method is commonly used to decide the best number of clusters (k) in K-Means clustering?

A Elbow method by plotting within-cluster sum of squares versus k

B Using the highest possible k to maximize clusters

C Choosing k based on the number of features in the dataset

D Randomly selecting k without evaluation

❓ Metrics

advanced

Evaluating K-Means Clustering Quality

Which metric measures how well-separated the clusters are in K-Means clustering?

A Mean squared error

B Silhouette score

C Accuracy score

D Cross-entropy loss

🔧 Debug

expert

Debugging K-Means Convergence Issue

You run K-Means on a dataset but notice the algorithm does not converge and runs indefinitely. Which is the most likely cause?

A The random_state parameter is not set

B The dataset contains only numerical features

C The number of clusters k is set larger than the number of unique data points

D The data is normalized before clustering