Hierarchical clustering in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the correct function for hierarchical clustering.

ML Python

from scipy.cluster.hierarchy import [1]

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

Afit_predict

Btrain_test_split

Clinkage

Dkmeans

Attempts:

3 left

💡 Hint

Common Mistakes

Importing kmeans instead of linkage

Trying to import fit_predict which is not in scipy.cluster.hierarchy

Confusing train_test_split with clustering functions

The linkage function is used to perform hierarchical clustering by computing the linkage matrix.

2fill in blank

medium

Complete the code to compute the linkage matrix using the 'ward' method.

ML Python

Z = linkage(data, method=[1])

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

A'average'

B'ward'

C'single'

D'complete'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'single' which links clusters by minimum distance

Using 'average' which links by average distance

Using 'complete' which links by maximum distance

The 'ward' method minimizes the variance of clusters being merged and is commonly used in hierarchical clustering.

3fill in blank

hard

Fix the error in the code to plot a dendrogram from the linkage matrix.

ML Python

dendrogram([1])
plt.show()

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

Alinkage

Bdata

Cplt

DZ

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw data instead of linkage matrix

Passing the plotting module instead of data

Passing the linkage function itself

The dendrogram function requires the linkage matrix Z as input to plot the hierarchical clustering tree.

4fill in blank

hard

Fill both blanks to create a dictionary of cluster labels for each sample using fcluster with a distance threshold.

ML Python

from scipy.cluster.hierarchy import fcluster
labels = fcluster([1], t=[2], criterion='distance')

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

AZ

Bdata

C5

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw data instead of linkage matrix

Using a string instead of a number for threshold

Confusing the order of arguments

The fcluster function takes the linkage matrix Z and a threshold t to assign cluster labels based on distance.

5fill in blank

hard

Fill all three blanks to compute the linkage matrix, plot the dendrogram, and assign cluster labels with max clusters = 3.

ML Python

Z = [1](data, method='ward')
dendrogram([2])
labels = fcluster(Z, t=[3], criterion='maxclust')

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

Alinkage

BZ

C3

Dfcluster

Attempts:

3 left

💡 Hint

Common Mistakes

Using fcluster instead of linkage for the first blank

Passing data instead of linkage matrix to dendrogram

Using distance threshold instead of max clusters in fcluster

First, compute the linkage matrix Z using linkage. Then plot the dendrogram using Z. Finally, assign cluster labels with fcluster using t=3 to get 3 clusters.

Practice

1. What is the main idea behind hierarchical clustering in machine learning?

easy

A. It uses a neural network to classify data points into clusters.

B. It assigns data points to clusters based on a fixed number of clusters.

C. It builds a tree of clusters by joining or splitting data points step by step.

D. It randomly assigns data points to clusters without any order.

Solution

1. Step 1: Understand hierarchical clustering concept

   Hierarchical clustering creates a tree-like structure by either merging or splitting data points stepwise.

2. Step 2: Compare with other clustering methods

   Unlike fixed cluster methods, it does not require a preset number of clusters and builds a hierarchy instead.

3. Final Answer:

   It builds a tree of clusters by joining or splitting data points step by step. -> Option C

4. Quick Check:

   Hierarchical clustering = stepwise tree building [OK]

Hint: Think of building a family tree of data points step by step [OK]

Common Mistakes:

• Confusing hierarchical clustering with k-means clustering
• Thinking it requires a fixed number of clusters upfront
• Believing it assigns clusters randomly

2. Which of the following Python code snippets correctly imports the function for hierarchical clustering from the SciPy library?

easy

A. from scipy.cluster.hierarchy import linkage

B. import sklearn.cluster.hierarchical as hc

C. from scipy.cluster import kmeans

D. import hierarchical from scipy.cluster

Solution

1. Step 1: Identify correct import for hierarchical clustering

   The function linkage from scipy.cluster.hierarchy is used for hierarchical clustering.

2. Step 2: Check other options for errors

   A has invalid syntax, B uses wrong module name (sklearn.cluster.hierarchical does not exist), C imports kmeans (not hierarchical).

3. Final Answer:

   from scipy.cluster.hierarchy import linkage -> Option A

4. Quick Check:

   Correct import = from scipy.cluster.hierarchy import linkage [OK]

Hint: Remember SciPy's hierarchy module has linkage function [OK]

Common Mistakes:

• Using sklearn.cluster.hierarchical which does not exist
• Confusing kmeans import with hierarchical clustering
• Incorrect import syntax

3. Given the following Python code using SciPy for hierarchical clustering, what will be the output of print(Z)?

from scipy.cluster.hierarchy import linkage
import numpy as np

X = np.array([[1, 2], [1, 4], [5, 2], [5, 4]])
Z = linkage(X, method='single')
print(Z)

medium

A. A 3x4 array showing the order and distance of cluster merges

B. A 4x4 distance matrix of all points

C. A list of cluster labels for each point

D. An error because method 'single' is invalid

Solution

1. Step 1: Understand linkage output format

   The linkage function returns a matrix with rows showing which clusters merged, their distance, and cluster size.

2. Step 2: Check method and output shape

   Method 'single' is valid. For 4 points, output is (4-1)=3 rows and 4 columns.

3. Final Answer:

   A 3x4 array showing the order and distance of cluster merges -> Option A

4. Quick Check:

   linkage output = cluster merges matrix [OK]

Hint: linkage returns merge info, not labels or distance matrix [OK]

Common Mistakes:

• Expecting cluster labels instead of merge matrix
• Confusing linkage output with distance matrix
• Thinking 'single' method is invalid

4. You run this code but get an error:

from scipy.cluster.hierarchy import dendrogram
import numpy as np

X = np.array([[1, 2], [3, 4], [5, 6]])
dendrogram(X)

What is the problem and how to fix it?

medium

A. The array X has wrong shape; reshape it to 1D before dendrogram().

B. You must pass a linkage matrix, not raw data, to dendrogram(). Use linkage() first.

C. dendrogram() requires labels argument; add labels=['a','b','c'].

D. You need to import dendrogram from sklearn.cluster, not scipy.

Solution

1. Step 1: Identify dendrogram input requirements

   dendrogram() expects a linkage matrix, not raw data points.

2. Step 2: Fix by computing linkage first

   Use linkage() on X to get the linkage matrix, then pass it to dendrogram().

3. Final Answer:

   You must pass a linkage matrix, not raw data, to dendrogram(). Use linkage() first. -> Option B

4. Quick Check:

   dendrogram input = linkage matrix [OK]

Hint: Always run linkage() before dendrogram() [OK]

Common Mistakes:

• Passing raw data directly to dendrogram()
• Thinking dendrogram needs labels argument
• Importing dendrogram from wrong library

5. You want to cluster customer data using hierarchical clustering but do not know how many clusters to choose. Which approach helps you decide the number of clusters without setting it upfront?

hard

A. Use PCA to reduce dimensions before clustering.

B. Use k-means clustering instead with a fixed k value.

C. Randomly assign clusters and check accuracy.

D. Cut the dendrogram at a chosen distance threshold to form clusters.

Solution

1. Step 1: Understand hierarchical clustering flexibility

   Hierarchical clustering builds a tree and allows cutting at any distance to form clusters.

2. Step 2: Use distance threshold to decide clusters

   By choosing a height to cut the dendrogram, you get clusters without presetting their number.

3. Final Answer:

   Cut the dendrogram at a chosen distance threshold to form clusters. -> Option D

4. Quick Check:

   Distance threshold cutting = flexible cluster count [OK]

Hint: Cut dendrogram by distance, not fixed cluster count [OK]

Common Mistakes:

• Thinking hierarchical clustering needs fixed cluster count upfront
• Confusing with k-means clustering method
• Ignoring dendrogram visualization for cluster choice