PCA - final exam review Questions and
Answers with Verified Solutions | Latest
Updated 2026
Objective of Dimensionality Simplify Dataset, reduce computational
Reduction requirements, and minimize the risk of
overfitting
What is feature selection in Keeping only the features that contribute
dimensionality reduction? the most to the prediction variable or
output you're interested in. Remove
redundancies
What is latent feature Creating new combinations of attributes
extraction? that capture the essential information in
the data with fewer dimensions.
What is principal component PCA is a multivariate statistical technique
analysis? to reduce higher dimensional data to
lower dimensions, remove noise, and
extract crucial information such as
features and attributes from large
amounts of data.
What are the general Data reduction and interpretation.
objectives of principal
component analysis?
, Does principal component No, there is no separation into
analysis separate dependent dependent and independent variables.
and independent variables?
What does principal A smaller set of uncorrelated variables
component analysis transform called principal components.
correlated variables into?
What is a common use of It is often used as the first step in factor
principal component analysis? analysis.
Is PCA supervised or unsupervised
unsupervised?
What does PCA transform Transforms them into uncorrelated
correlated variables into variables
Standardization PCA 1st step of PCA, scale each feature to
have a mean of 0 and a standard
deviation of 1, ensuring all variables
contribute equally
Steps of PCA algorithm 1. Normalize data.
2. Calculate the covariance matrix of
normalized data.
3. Calculate the eigenvalues and
eigenvectors of the calculated
covariance matrix.
4. Project Data onto New Axes
Answers with Verified Solutions | Latest
Updated 2026
Objective of Dimensionality Simplify Dataset, reduce computational
Reduction requirements, and minimize the risk of
overfitting
What is feature selection in Keeping only the features that contribute
dimensionality reduction? the most to the prediction variable or
output you're interested in. Remove
redundancies
What is latent feature Creating new combinations of attributes
extraction? that capture the essential information in
the data with fewer dimensions.
What is principal component PCA is a multivariate statistical technique
analysis? to reduce higher dimensional data to
lower dimensions, remove noise, and
extract crucial information such as
features and attributes from large
amounts of data.
What are the general Data reduction and interpretation.
objectives of principal
component analysis?
, Does principal component No, there is no separation into
analysis separate dependent dependent and independent variables.
and independent variables?
What does principal A smaller set of uncorrelated variables
component analysis transform called principal components.
correlated variables into?
What is a common use of It is often used as the first step in factor
principal component analysis? analysis.
Is PCA supervised or unsupervised
unsupervised?
What does PCA transform Transforms them into uncorrelated
correlated variables into variables
Standardization PCA 1st step of PCA, scale each feature to
have a mean of 0 and a standard
deviation of 1, ensuring all variables
contribute equally
Steps of PCA algorithm 1. Normalize data.
2. Calculate the covariance matrix of
normalized data.
3. Calculate the eigenvalues and
eigenvectors of the calculated
covariance matrix.
4. Project Data onto New Axes
