Unsupervised Learning QUIZ QUESTIONS

Question: 1

What is the primary difference between supervised and unsupervised learning?

Supervised learning requires labeled data, while unsupervised learning does not

Supervised learning is used for classification, while unsupervised learning is used for regression

Supervised learning is deterministic, while unsupervised learning is stochastic

Supervised learning is used for online learning, while unsupervised learning is used for offline learning

Question: 2

What is the purpose of dimensionality reduction in unsupervised learning?

To reduce the number of features in the dataset, making it easier to visualize and analyze

To increase the number of features in the dataset, making it more informative

To remove irrelevant features from the dataset, improving model performance

To optimize the clustering algorithm used in unsupervised learning

Question: 3

Which of the following is a common application of unsupervised learning?

Image classification

Sentiment analysis

Anomaly detection

Predictive maintenance

Question: 4

What is the primary goal of clustering algorithms in unsupervised learning?

To predict a continuous target variable

To classify data into known categories

To group similar data points together

To optimize a reward function

Question: 5

Which of the following is an example of a dimensionality reduction technique in unsupervised learning?

Principal component analysis (PCA)

Linear discriminant analysis (LDA)

K-means clustering

Decision trees

Question: 6

What is a common challenge in unsupervised learning?

Overfitting

Underfitting

Evaluating model performance

Handling missing data

Question: 7

Which of the following is a density-based clustering algorithm?

K-means clustering

DBSCAN

Hierarchical clustering

Spectral clustering

Question: 8

What is the primary advantage of hierarchical clustering over other clustering methods?

It can handle missing data

It produces a dendrogram that shows the nested structure of the clusters

It can automatically determine the optimal number of clusters

It is more efficient in terms of computational complexity

Question: 9

Which of the following unsupervised learning algorithms is based on the idea of transforming the data into a lower-dimensional space while preserving the pairwise distances between data points?

Principal component analysis (PCA)

t-distributed stochastic neighbor embedding (t-SNE)

K-means clustering

DBSCAN

Question: 10

What is the primary goal of autoencoders in unsupervised learning?

To learn a compressed representation of the input data

To classify input data into known categories

To predict a continuous target variable

To optimize a reward function

Question: 11

Which of the following is an example of a clustering evaluation metric?

Accuracy

F1-score

Silhouette score

Mean squared error

Question: 12

In unsupervised learning, what is the purpose of using the elbow method?

To determine the optimal number of clusters

To select the optimal features for clustering

To choose the best clustering algorithm

To evaluate the performance of a clustering algorithm

Question: 13

What is the primary difference between top-down and bottom-up hierarchical clustering?

Top-down clustering starts with a single cluster and iteratively splits it, while bottom-up clustering starts with individual data points and iteratively merges them

Top-down clustering is deterministic, while bottom-up clustering is stochastic

Top-down clustering is more computationally efficient, while bottom-up clustering is more accurate

Top-down clustering is used for online learning, while bottom-up clustering is used for offline learning

Question: 14

Which of the following unsupervised learning algorithms is suitable for finding non-linear structures in the data?

Principal component analysis (PCA)

K-means clustering

Isomap

Spectral clustering

Question: 15

What is a common application of autoencoders in unsupervised learning?

Image segmentation

Image compression

Image classification

Image enhancement

Question: 16

Which of the following unsupervised learning algorithms can be used for both clustering and dimensionality reduction?

K-means clustering

DBSCAN

Spectral clustering

t-distributed stochastic neighbor embedding (t-SNE)

Question: 17

What is the primary advantage of using Gaussian mixture models (GMMs) for clustering over other clustering algorithms?

They can handle missing data

They can model clusters with different shapes, sizes, and orientations

They are more computationally efficient

They can automatically determine the optimal number of clusters

Question: 18

What is the primary goal of outlier detection algorithms in unsupervised learning?

To classify data into known categories

To group similar data points together

To identify unusual or unexpected data points

To optimize a reward function

Question: 19

Which of the following is a common application of dimensionality reduction techniques in unsupervised learning?

Image recognition

Data visualization

Sentiment analysis

Text summarization

Question: 20

In the context of unsupervised learning, what is a "topic model"?

A model that groups similar words together based on their co-occurrence patterns in a collection of documents

A model that classifies documents into predefined categories based on their content

A model that generates new documents based on a given topic

A model that extracts key phrases from a collection of documents

Question: 21

Which of the following unsupervised learning algorithms is most suitable for finding clusters with arbitrary shapes?

K-means clustering

DBSCAN

Gaussian mixture models (GMMs)

Hierarchical clustering

Question: 22

Which of the following is an example of a manifold learning technique in unsupervised learning?

Principal component analysis (PCA)

t-distributed stochastic neighbor embedding (t-SNE)

K-means clustering

DBSCAN

Question: 23

What is a common challenge when applying clustering algorithms to high-dimensional data?

Overfitting

The curse of dimensionality

Evaluating model performance

Handling missing data

Question: 24

What is the primary goal of feature selection in unsupervised learning?

To reduce the number of features in the dataset, making it easier to visualize and analyze

To increase the number of features in the dataset, making it more informative

To remove irrelevant features from the dataset, improving model performance

To optimize the clustering algorithm used in unsupervised learning

Question: 25

Which of the following is a similarity measure commonly used in unsupervised learning?

Euclidean distance

Pearson correlation coefficient

Cosine similarity

All of the above

Question: 26

Which of the following clustering algorithms is most sensitive to the initial placement of cluster centroids?

K-means clustering

DBSCAN

Gaussian mixture models (GMMs)

Hierarchical clustering

Question: 27

In the context of unsupervised learning, what is "semi-supervised learning"?

A learning approach that combines both labeled and unlabeled data

A learning approach that requires only a small amount of labeled data

A learning approach that uses a combination of supervised and unsupervised learning algorithms

A learning approach that involves active learning and human-in-the-loop feedback

Question: 28

What is a common application of clustering algorithms in unsupervised learning?

Customer segmentation

Image recognition

Text classification

Speech recognition

Question: 29

What is the primary goal of unsupervised learning?

To classify data into known categories

To find hidden patterns or structures in data

To optimize a reward function

To predict future data points based on past observations

Question: 30

Which of the following is an example of an unsupervised learning algorithm?

Linear regression

Logistic regression

K-means clustering

Support vector machines