K-Means clustering in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the KMeans class from scikit-learn.

ML Python

from sklearn.cluster import [1]

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

AKMeans

BLinearRegression

CDecisionTreeClassifier

DRandomForest

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a different model class like LinearRegression.

Using a classifier class instead of a clustering class.

The KMeans class is used for K-Means clustering in scikit-learn.

2fill in blank

medium

Complete the code to create a KMeans model with 3 clusters.

ML Python

model = KMeans(n_clusters=[1])

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

A10

B1

C5

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 cluster which defeats the purpose of clustering.

Using too many clusters without reason.

Setting n_clusters=3 tells the model to find 3 clusters.

3fill in blank

hard

Fix the error in the code to fit the KMeans model on data X.

ML Python

model.[1](X)

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

Afit

Bpredict

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict() before fitting the model.

Using transform() which changes data but does not train.

The fit() method trains the KMeans model on the data.

4fill in blank

hard

Fill both blanks to create a dictionary of cluster labels for each data point.

ML Python

labels_dict = {i: model.[1][i] for i in range([2])}

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

Alabels_

Blen(X)

Ccluster_centers_

Dinertia_

Attempts:

3 left

💡 Hint

Common Mistakes

Using cluster_centers_ which gives coordinates, not labels.

Using model.n_clusters instead of len(X).

model.labels_ gives the cluster label for each point. range(len(X)) iterates over data points.

5fill in blank

hard

Fill all three blanks to compute and print the silhouette score for the clustering.

ML Python

from sklearn.metrics import [1]
score = [2](X, model.[3])
print('Silhouette Score:', score)

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

Asilhouette_score

Blabels_

Cpredict

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit_predict() which fits and predicts but is not needed here.

Using model.predict() which returns predicted labels but silhouette_score expects labels.

silhouette_score is imported and called with data X and cluster labels model.labels_.

Practice

1. What does the n_clusters parameter in K-Means control?

easy

A. The initial random seed for cluster centers

B. The maximum number of iterations for the algorithm

C. The number of groups (clusters) to find in the data

D. The distance metric used to measure similarity

Solution

1. Step 1: Understand the role of n_clusters

   The n_clusters parameter tells K-Means how many groups to split the data into.

2. Step 2: Compare with other parameters

   Other parameters control iterations, distance, or randomness, but n_clusters specifically sets group count.

3. Final Answer:

   The number of groups (clusters) to find in the data -> Option C

4. Quick Check:

   n_clusters = number of groups [OK]

Hint: Remember: n_clusters means how many groups you want [OK]

Common Mistakes:

• Confusing n_clusters with max iterations
• Thinking n_clusters sets distance metric
• Assuming n_clusters controls randomness

2. Which of the following is the correct way to import KMeans from scikit-learn in Python?

easy

A. from sklearn import KMeans.cluster

B. import KMeans from sklearn.cluster

C. import sklearn.cluster.KMeans

D. from sklearn.cluster import KMeans

Solution

1. Step 1: Recall Python import syntax

   Python uses 'from module import class' to import specific classes.

2. Step 2: Match correct syntax for KMeans

   The correct import is from sklearn.cluster import KMeans.

3. Final Answer:

   from sklearn.cluster import KMeans -> Option D

4. Quick Check:

   Correct import syntax = from sklearn.cluster import KMeans [OK]

Hint: Use 'from module import class' for importing classes [OK]

Common Mistakes:

• Using wrong import order
• Trying to import with 'import' and 'from' combined incorrectly
• Using dot notation in import statement

3. Given the code below, what will be the output of print(kmeans.labels_)?

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_)

medium

A. [0 0 0 1 1 1]

B. [1 1 1 0 0 0]

C. [0 1 0 1 0 1]

D. [1 0 1 0 1 0]

Solution

1. Step 1: Understand data grouping

   The first three points are close together near (1, y), the last three near (10, y), so KMeans will group first three as one cluster and last three as another.

2. Step 2: Check label assignment

   KMeans assigns cluster labels starting from 0. The first cluster gets label 0, second cluster label 1. But the cluster near (10, y) is assigned label 0 and near (1, y) label 1 due to initialization and fitting.

3. Final Answer:

   [1 1 1 0 0 0] -> Option B

4. Quick Check:

   Close points share label 0 or 1 [OK]

Hint: Clusters group close points; labels start at 0 [OK]

Common Mistakes:

• Assuming labels start at 1
• Mixing cluster order
• Ignoring spatial grouping

4. What is wrong with the following K-Means code snippet?

from sklearn.cluster import KMeans
X = [[1, 2], [1, 4], [1, 0]]
kmeans = KMeans(n_clusters=4)
kmeans.fit(X)
print(kmeans.labels_)

medium

A. The number of clusters (4) is greater than the number of data points (3)

B. The data X should be a numpy array, not a list

C. The KMeans class requires a random_state parameter

D. The fit method should be called with labels, not data

Solution

1. Step 1: Check cluster count vs data points

   There are only 3 data points but 4 clusters requested, which is invalid because you cannot have more clusters than points.

2. Step 2: Validate other code parts

   Using a list for X is allowed, random_state is optional, and fit expects data, not labels.

3. Final Answer:

   The number of clusters (4) is greater than the number of data points (3) -> Option A

4. Quick Check:

   Clusters ≤ data points [OK]

Hint: Clusters cannot exceed number of data points [OK]

Common Mistakes:

• Thinking data must be numpy array
• Believing random_state is mandatory
• Confusing fit input as labels

5. You have a dataset with 3 natural groups, but you run K-Means with n_clusters=5. What is the likely effect on the clustering result?

hard

A. K-Means will split some natural groups into smaller clusters, causing over-segmentation

B. K-Means will ignore extra clusters and only find 3 groups

C. K-Means will fail to run and throw an error

D. K-Means will merge groups to reduce clusters to 3 automatically

Solution

1. Step 1: Understand cluster count effect

   Setting more clusters than natural groups causes K-Means to split existing groups into smaller parts.

2. Step 2: Check K-Means behavior

   K-Means does not reduce cluster count automatically or throw error if clusters exceed natural groups; it always finds exactly n_clusters.

3. Final Answer:

   K-Means will split some natural groups into smaller clusters, causing over-segmentation -> Option A

4. Quick Check:

   More clusters than groups = splitting groups [OK]

Hint: More clusters than groups splits groups into smaller parts [OK]

Common Mistakes:

• Thinking K-Means auto-adjusts cluster count
• Expecting error when clusters > natural groups
• Assuming extra clusters are ignored