Why unsupervised learning finds hidden patterns in ML Python - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why does unsupervised learning find hidden patterns?

Unsupervised learning algorithms analyze data without labeled answers. Why can they find hidden patterns in data?

A Because they group or organize data based on similarities or structures found within the data

B Because they predict future values based on past labeled examples

C Because they randomly assign categories without analyzing data

D Because they use labels to guide the learning process

❓ Predict Output

intermediate

Output of K-means clustering on simple data

What is the output cluster assignment for the points after running K-means with 2 clusters?

ML Python

from sklearn.cluster import KMeans
import numpy as np

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

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

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

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

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

❓ Model Choice

advanced

Best unsupervised model for dimensionality reduction

You want to reduce the number of features in your dataset while preserving important information. Which unsupervised model is best suited for this?

A Principal Component Analysis (PCA)

B K-means clustering

C Linear Regression

D Decision Tree

❓ Metrics

advanced

Evaluating clustering quality without labels

Which metric can be used to evaluate the quality of clusters found by an unsupervised algorithm when no true labels are available?

A Accuracy score

B Silhouette score

C Mean squared error

D Cross-entropy loss

🔧 Debug

expert

Why does this PCA code raise an error?

What error does the following PCA code raise and why?

ML Python

from sklearn.decomposition import PCA
import numpy as np

X = np.array([[1, 2], [3, 4], [5, 6]])
pca = PCA(n_components=4)
pca.fit(X)

A AttributeError: 'PCA' object has no attribute 'fit'

B TypeError: n_components must be an integer

C ValueError: n_components must be between 0 and min(n_samples, n_features) inclusive

D No error, code runs successfully