Unsupervised Learning QUIZ (MCQ QUESTIONS AND ANSWERS)

Principal component analysis (PCA)

K-means clustering

Isomap

Spectral clustering

Customer segmentation

Image recognition

Text classification

Speech recognition

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

K-means clustering

DBSCAN

Gaussian mixture models (GMMs)

Hierarchical clustering

Euclidean distance

Pearson correlation coefficient

Cosine similarity

All of the above

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

Overfitting

The curse of dimensionality

Evaluating model performance

Handling missing data

Principal component analysis (PCA)

t-distributed stochastic neighbor embedding (t-SNE)

K-means clustering

DBSCAN

K-means clustering

DBSCAN

Gaussian mixture models (GMMs)

Hierarchical clustering

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

Image recognition

Data visualization

Sentiment analysis

Text summarization

To classify data into known categories

To group similar data points together

To identify unusual or unexpected data points

To optimize a reward function

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

K-means clustering

DBSCAN

Spectral clustering

t-distributed stochastic neighbor embedding (t-SNE)

Image segmentation

Image compression

Image classification

Image enhancement

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

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

Accuracy

F1-score

Silhouette score

Mean squared error

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

Principal component analysis (PCA)

t-distributed stochastic neighbor embedding (t-SNE)

K-means clustering

DBSCAN

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

K-means clustering

DBSCAN

Hierarchical clustering

Spectral clustering

Overfitting

Underfitting

Evaluating model performance

Handling missing data

Principal component analysis (PCA)

Linear discriminant analysis (LDA)

K-means clustering

Decision trees

To predict a continuous target variable

To classify data into known categories

To group similar data points together

To optimize a reward function

Image classification

Sentiment analysis

Anomaly detection

Predictive maintenance

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

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

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

Linear regression

Logistic regression

K-means clustering

Support vector machines