What is PCA algorithm used for? A. Supervised learning * B. Unsupervised learning C. Feature scaling D. Data clustering

PCA can be thought of as a process of ______ A. Feature scaling B. Feature selection C. Dimensionality reduction * D. Model selection

What type of learning does PCA belong to? A. Supervised learning B. Unsupervised learning * C. Reinforcement learning D. Semisupervised learning

Which of the following is NOT a use for PCA? A. Feature engineering B. Noise reduction * C. Visualizing data D. Dimensionality reduction

What is the main task of PCA? A. Constructing new features B. Finding clusters in data C. Extracting important features * D. Classification of data

PCA maximizes the distances between ______ A. Centroids B. Outliers C. Feature points D. Principal components *

What is the Step 4 of the PCA algorithm? A. Calculating the eigenvalues * B. Constructing new features C. Determining the principal components D. Finding clusters in data

What is the main advantage of PCA over other dimensionality reduction techniques? A. It can only be used with numerical data B. It preserves the structure of the data * C. It can be used with large datasets D. It is able to remove redundant data

What is the goal of PCA? A. To reduce the size of the dataset B. To get the best predictive accuracy C. To maximize the variance of each principal component * D. To make data more interpretable

What is the limitation of PCA? A. It is computationally expensive B. It fails to identify outliers C. It only works with numerical data * D. It is unable to handle large datasets