Principal Component Analysis (PCA): A Comprehensive Guide for Unsupervised Learning

Question 1

In dimensionality reduction, what is a primary advantage of Linear Discriminant Analysis (LDA) over Principal Component Analysis (PCA)?

Accepted Answer

LDA prioritizes preserving class separation.

Answer

LDA effectively handles non-linear relationships.

Answer

LDA operates with fewer computational resources.

Answer

LDA exhibits greater resilience to outliers.

Question 2

What's the main purpose of dimensionality reduction?

Accepted Answer

Reducing features in a dataset while keeping important info.

Answer

Increasing features in a dataset to capture more data.

Answer

Changing numerical features to categorical features.

Question 3

Identify the primary objective of Principal Component Analysis (PCA) in machine learning.

Accepted Answer

Dimensionality reduction while retaining critical information

Answer

Enhancing classification model performance

Answer

Identifying distinct clusters within data

Answer

Fitting a non-linear model to data

Question 4

What fundamental concept underlies Principal Component Analysis (PCA)?

Accepted Answer

Identifying directions of maximum variance in data

Answer

Fitting a linear model to data

Answer

Calculating the mean and covariance of data

Answer

Clustering data points into distinct groups

Question 5

Select the primary advantage associated with Principal Component Analysis (PCA).

Accepted Answer

Enhanced data visualization by projecting data into a lower-dimensional space

Answer

Increased data accuracy

Answer

Improved classification model accuracy

Answer

Boosted regression accuracy

Question 6

Describe the mathematical relationship between the original data and the principal components in PCA.

Accepted Answer

Linear combination

Answer

Non-linear transformation

Answer

Random mapping

Answer

Orthogonal projection

Question 7

Which measure is used to determine the significance of principal components in PCA?

Accepted Answer

Eigenvalues

Answer

Correlation coefficients

Answer

Variance

Answer

Loadings

Question 8

How can PCA be effectively utilized for feature extraction?

Accepted Answer

By reducing dimensionality while preserving key information

Answer

By improving the efficiency of clustering algorithms

Answer

By enhancing the accuracy of classification models

Question 9

Describe the process involved in determining the optimal number of principal components in PCA.

Accepted Answer

Using techniques like the scree plot or cumulative variance

Answer

Trial and error

Answer

Based on domain knowledge

Question 10

In what way does PCA contribute to improved data visualization?

Accepted Answer

By projecting data onto a lower-dimensional space for easier visual representation

Answer

By fitting a non-linear model to the data

Answer

By clustering data points into distinct groups

Answer

By identifying outliers in the data

Question 11

Which of the following is NOT a key concept associated with Principal Component Analysis (PCA)?

Accepted Answer

Supervised Learning

Answer

Eigenvalues

Answer

Dimensionality Reduction

Answer

Eigenvectors

Question 12

What is the primary goal of using PCA in machine learning?

Accepted Answer

Reduce data dimensionality while preserving key information

Answer

Identify patterns in data

Answer

Predict future outcomes

Answer

Classify data points

Question 13

What type of transformation is applied to the original data in PCA?

Accepted Answer

Linear Transformation

Answer

Nonlinear Transformation

Answer

Affine Transformation

Answer

Perspective Transformation

Question 14

Which metric is commonly used to determine the optimal number of principal components to retain?

Accepted Answer

Scree Plot

Answer

Accuracy Score

Answer

F1 Score

Answer

ROC Curve

Question 15

What is the role of eigenvectors in PCA?

Accepted Answer

Define the directions of maximum variance in the data

Answer

Calculate the eigenvalues

Answer

Represent the original data points

Question 16

Which of the following is **NOT** a key concept of Principal Component Analysis (PCA)?

Accepted Answer

Supervised learning

Answer

Dimensionality reduction

Answer

Variance maximization

Answer

Data transformation

Question 17

What is the primary objective of PCA?

Accepted Answer

Identifying directions of maximum variance in data

Answer

Clustering data points into distinct groups

Answer

Predicting future outcomes based on historical data

Answer

Extracting patterns and relationships from text data

Question 18

How does PCA reduce dimensionality?

Accepted Answer

Projecting data onto a lower-dimensional subspace

Answer

Converting data into binary representation

Answer

Removing insignificant features

Answer

Clustering data points based on similarity

Question 19

What is the significance of eigenvalues and eigenvectors in PCA?

Accepted Answer

Eigenvalues represent variance, eigenvectors represent directions of maximum variance

Answer

Eigenvalues represent directions of maximum variance, eigenvectors represent variance

Answer

Eigenvalues and eigenvectors both represent variance

Question 20

When is PCA considered an appropriate data analysis technique?

Accepted Answer

When data has high dimensionality with correlated features

Answer

When data is perfectly linear and independent

Answer

When data is sparse with outliers

Question 21

What are the typical outputs of PCA?

Accepted Answer

Principal components, eigenvalues, variance explained

Answer

Cluster labels, decision boundaries, classification accuracy

Answer

Regression coefficients, p-values, confidence intervals

Question 22

How does variance explained impact the effectiveness of PCA?

Accepted Answer

Higher variance explained indicates more effective dimensionality reduction

Answer

Variance explained is unaffected by the number of principal components

Answer

Lower variance explained indicates better data separation

Question 23

What is a key advantage of using PCA in data analysis?

Accepted Answer

Enhanced data visualization and interpretation

Answer

Can only be applied to large datasets

Answer

Suitable for both supervised and unsupervised learning tasks

Answer

Guaranteed improvement in model performance