Unsupervised Algorithm

What is the primary objective of unsupervised learning?

• Minimize prediction errors

• Maximize accuracy on labeled data

• Discover underlying patterns in data

• Optimize feature engineering

Which of the following is NOT an application of unsupervised learning?

• Image classification

• Clustering customer segments

• Anomaly detection

• Dimensionality reduction

What is the main difference between supervised and unsupervised learning?

• Unsupervised learning requires labeled data for training

• Supervised learning does not involve feature extraction

• Unsupervised learning is used for classification tasks

• Supervised learning relies on ground truth labels for training

Which algorithm is commonly used for dimensionality reduction in unsupervised learning?

• K-Nearest Neighbors

• Random Forest

• Principal Component Analysis (PCA)

• Decision Trees

What is the goal of clustering in unsupervised learning?

• Classify data into predefined categories

• Predict target values for new data points

• Identify outliers in the data

• Group similar data points together based on their features

Which evaluation metric is often used to assess clustering performance?

• F1 Score

• Mean Absolute Error (MAE)

• Adjusted Rand Index (ARI)

• Accuracy

In hierarchical clustering, how are data points initially represented?

• As individual clusters

• As a single big cluster

• As a dendrogram

• As a graph

Which unsupervised learning algorithm tries to preserve the pairwise distances between data points in a lower-dimensional space?

• K-Means

• Hierarchical clustering

• t-Distributed Stochastic Neighbor Embedding (t-SNE)

• DBSCAN

What is the purpose of the "elbow method" in K-Means clustering?

• To find the number of clusters in the data

• To calculate the inertia of the clusters

• To measure the silhouette score

• To evaluate the clustering accuracy

Which unsupervised learning algorithm is based on the concept of "density" and can identify noise and outliers?

• K-Means

• Hierarchical clustering

• DBSCAN

• Principal Component Analysis (PCA)