Dimensionality Reduction: A Guide to Feature Reduction in Machine Learning

Question 1

Which of the following is NOT an advantage of dimensionality reduction in machine learning?

Accepted Answer

Improved model precision

Answer

Enhanced model understanding

Answer

Lower computational costs

Answer

Simplified data visualization

Question 2

What is a potential drawback of using dimensionality reduction techniques?

Accepted Answer

Loss of valuable information during data projection into a lower-dimensional space

Answer

Improved machine learning model performance due to fewer features

Answer

Low computational cost regardless of technique or dataset size

Question 3

What is the primary purpose of dimensionality reduction?

Accepted Answer

To reduce the number of features in a dataset while preserving as much information as possible

Answer

To improve the accuracy of machine learning models

Answer

To reduce computational costs

Answer

To enhance data visualization capabilities

Question 4

Which of the following is NOT an advantage of dimensionality reduction?

Accepted Answer

Loss of potentially valuable information

Answer

Improved data visualization

Answer

Enhanced interpretability

Answer

Reduced computational time

Question 5

What is the role of `n_components` in PCA?

Accepted Answer

To specify the number of principal components to retain

Answer

To determine the variance to be explained by the retained components

Answer

To specify the regularization parameter

Answer

To set the learning rate for the PCA algorithm

Question 6

Which of the following is a limitation of LDA?

Accepted Answer

Assumes a Gaussian distribution of data

Answer

Suitable for both supervised and unsupervised learning

Answer

Preserves interpretability of the original features

Answer

Can handle non-linear relationships between features

Question 7

What is the purpose of perplexity in t-SNE?

Accepted Answer

Controls the trade-off between local and global data structure preservation

Answer

Determines the number of nearest neighbors to consider

Answer

Sets the learning rate for the t-SNE algorithm

Answer

Specifies the number of iterations

Question 8

In PCA, what objective function is utilized to determine principal components?

Accepted Answer

Maximizing the projected data's variance

Answer

Minimizing the reconstruction error between original and projected data

Answer

Minimizing the dimensionality of the projected data

Answer

Maximizing inter-point distances in the projected data

Question 9

Which dimensionality reduction technique is non-linear?

Accepted Answer

t-SNE

Answer

SVD

Answer

PCA

Answer

Linear Discriminant Analysis (LDA)

Question 10

What is the purpose of setting a perplexity parameter in t-SNE?

Accepted Answer

Controlling the smoothness of the embedded manifold

Answer

Determining the target dimensionality

Answer

Preventing overfitting

Answer

Initializing embedded data coordinates

Question 11

What is the primary objective of dimensionality reduction in machine learning?

Accepted Answer

Enhancing the performance of machine learning models

Answer

Reducing data storage costs

Answer

Facilitating data visualization

Question 12

What is the fundamental distinction between PCA and LDA?

Accepted Answer

PCA is unsupervised, whereas LDA is supervised

Answer

PCA is less interpretable than LDA

Answer

LDA can reduce dimensionality to one dimension, whereas PCA cannot

Answer

PCA is applicable to non-linear data, whereas LDA is not

Question 13

How does dimensionality reduction impact data interpretability?

Accepted Answer

Can enhance interpretability by decreasing the number of features

Answer

Has no impact on data interpretability

Answer

Always decreases data interpretability

Question 14

Which technique is used to reduce the number of features in a dataset while retaining maximum information?

Accepted Answer

Dimensionality Reduction

Answer

Classification

Answer

Clustering

Answer

Regression

Question 15

Which dimensionality reduction technique effectively eliminates redundant features?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Factor Analysis

Answer

Independent Component Analysis (ICA)

Answer

Canonical Correlation Analysis (CCA)

Question 16

Identify a limitation associated with Linear Discriminant Analysis (LDA).

Accepted Answer

Assumes normally distributed data

Answer

Applicable to both classification and regression

Answer

Suitable for high-dimensional datasets

Answer

Can handle non-linear relationships

Question 17

Which dimensionality reduction technique is ideal for visualizing high-dimensional data?

Accepted Answer

Multidimensional Scaling (MDS)

Answer

Linear Discriminant Analysis (LDA)

Answer

Factor Analysis

Answer

Principal Component Analysis (PCA)

Question 18

Define the main objective of dimensionality reduction.

Accepted Answer

Reducing the number of features in a dataset while preserving maximum information.

Answer

Shortening the training time of machine learning models

Answer

Enhancing the accuracy of machine learning models

Question 19

Explain the 'curse of dimensionality'.

Accepted Answer

As the number of features in a dataset increases, the volume of the data space increases exponentially, making it challenging to analyze and visualize.

Answer

As the number of features increases, the accuracy of machine learning models decreases.

Question 20

Identify the dimensionality reduction technique that utilizes class labels during the transformation.

Accepted Answer

Linear Discriminant Analysis (LDA)

Answer

Multidimensional Scaling (MDS)

Answer

Independent Component Analysis (ICA)

Answer

Principal Component Analysis (PCA)

Question 21

Describe the fundamental difference between PCA and LDA.

Accepted Answer

PCA is an unsupervised technique, while LDA is a supervised technique.

Answer

PCA is suitable for high-dimensional datasets, while LDA is not.

Answer

PCA can capture non-linear relationships, while LDA cannot.

Question 22

Which of the following is NOT a benefit of dimensionality reduction?

Accepted Answer

Increases model complexity

Answer

Improves data visualization

Answer

Handles overfitting

Answer

Reduces computational complexity

Question 23

What is the key difference between supervised and unsupervised dimensionality reduction techniques?

Accepted Answer

Supervised techniques use labeled data while unsupervised techniques do not.

Answer

Supervised techniques are more accurate than unsupervised techniques.

Answer

Supervised techniques reduce the number of features while unsupervised techniques do not.

Question 24

What is the dimensionality of a dataset with 3 features?

Accepted Answer

3

Answer

2

Answer

4

Answer

5

Question 25

Which of the following is a type of non-linear dimensionality reduction technique?

Accepted Answer

t-distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Answer

Principal Component Analysis (PCA)

Question 26

What is the purpose of using a scree plot in dimensionality reduction?

Accepted Answer

To determine the optimal number of components to retain.

Answer

To visualize the distribution of data points

Answer

To identify outliers

Question 27

Which of the following is a parameter to consider when using Principal Component Analysis (PCA)?

Accepted Answer

Number of components

Answer

Learning rate

Answer

Regularization

Answer

Epochs

Question 28

Which of the following is NOT a dimensionality reduction technique?

Accepted Answer

Clustering

Answer

Singular Value Decomposition

Answer

Linear Discriminant Analysis

Answer

Principal Component Analysis

Question 29

What is the main advantage of Principal Component Analysis (PCA)?

Accepted Answer

It preserves the maximum variance in the data.

Answer

It is insensitive to outliers.

Answer

It can handle both numerical and categorical data.

Answer

It is computationally efficient.

Question 30

What is the purpose of Singular Value Decomposition (SVD)?

Accepted Answer

To reduce the dimensionality of a matrix.

Answer

To find the eigenvalues and eigenvectors of a matrix.

Answer

To perform matrix inversion.

Answer

To solve systems of linear equations.

Question 31

Which of the following is an ethical consideration when using dimensionality reduction?

Accepted Answer

It can lead to bias if the data is not representative.

Answer

It can make models more difficult to deploy.

Answer

It can increase the risk of overfitting.

Answer

It can violate privacy regulations.

Question 32

What is NOT a primary goal of dimensionality reduction?

Accepted Answer

Increasing the model's complexity.

Answer

Improving model performance.

Answer

Simplifying data visualization.

Answer

Reducing computational cost.

Question 33

Which dimensionality reduction technique is best known for preserving the variance of the original data?

Accepted Answer

Principal Component Analysis (PCA)

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Answer

Feature Selection using Univariate Statistics

Question 34

In PCA, the principal components are ordered based on their:

Accepted Answer

Explained variance.

Answer

Correlation with the target variable.

Answer

Number of features they represent.

Answer

Computational complexity.

Question 35

Which of these is a potential drawback of using dimensionality reduction techniques?

Accepted Answer

Loss of interpretability of the data.

Answer

Improved model efficiency.

Answer

Reduced data size.

Answer

Increased model complexity.

Question 36

Which dimensionality reduction technique is often used for visualizing high-dimensional data in two or three dimensions?

Accepted Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Answer

Principal Component Analysis (PCA)

Question 37

The goal of Linear Discriminant Analysis (LDA) is to find directions that:

Accepted Answer

Maximize the separation between classes.

Answer

Maximize the variance within each class.

Answer

Minimize the computational cost.

Answer

Preserve the original data distribution.

Question 38

Feature selection techniques are considered a form of dimensionality reduction because they:

Accepted Answer

Eliminate irrelevant features from the dataset.

Answer

Reduce the variance of the data.

Answer

Transform the original data into a lower-dimensional space.

Answer

Create new features from existing ones.

Question 39

Which of the following is NOT a typical criterion used for selecting features in feature selection methods?

Accepted Answer

Data distribution.

Answer

Feature importance.

Answer

Correlation with the target variable.

Question 40

Which of the following is **not** a benefit of dimensionality reduction?

Accepted Answer

Increases computational complexity

Answer

Accelerates model training

Answer

Reduces overfitting

Answer

Improves data visualization

Question 41

Which of the following is a key assumption of Principal Component Analysis (PCA)?

Accepted Answer

Features are linearly correlated

Answer

Features are independent

Answer

Data is normally distributed

Answer

Data is high-dimensional

Question 42

Which of the following is a non-linear dimensionality reduction technique?

Accepted Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Principal Component Analysis (PCA)

Answer

Singular Value Decomposition (SVD)

Answer

Linear Discriminant Analysis (LDA)

Question 43

What is the dimensionality of a dataset after applying Linear Discriminant Analysis (LDA)?

Accepted Answer

Equal to the number of classes minus 1

Answer

Equal to the original dimensionality

Answer

Cannot be determined without additional information

Answer

Equal to the number of features

Question 44

What is a key consideration when determining the optimal number of components to retain in PCA?

Accepted Answer

Specific dataset and task

Answer

Always 1

Answer

Equal to the number of observations

Answer

Equal to the number of features

Question 45

Which of the following is a supervised dimensionality reduction technique?

Accepted Answer

Linear Discriminant Analysis (LDA)

Answer

PCA

Answer

SVD

Answer

t-SNE

Question 46

What is the primary objective of dimensionality reduction?

Accepted Answer

To represent data in a lower-dimensional space while preserving significant information

Answer

To reduce the computational cost of data processing

Answer

To increase the accuracy of machine learning models

Question 47

Which of the following is not a primary purpose of dimensionality reduction?

Accepted Answer

Increasing the complexity of the model

Answer

Reducing computational cost

Answer

Improving model performance

Question 48

In Principal Component Analysis (PCA), what is the main interpretation of the eigenvalues?

Accepted Answer

Amount of variance explained by each principal component

Answer

Correlation between original features and principal components

Answer

Distance between data points in the reduced space

Question 49

When dealing with high-dimensional data with non-linear relationships between features, which technique is generally more appropriate for dimensionality reduction?

Accepted Answer

t-SNE

Answer

LDA

Answer

PCA

Question 50

What is the main disadvantage of using Linear Discriminant Analysis (LDA) for dimensionality reduction?

Accepted Answer

It assumes linear separability between classes

Answer

It requires a large number of features for optimal performance

Answer

It can be computationally expensive for large datasets

Answer

It is not effective for reducing high-dimensional data

Question 51

Which of the following is a common metric used to evaluate the effectiveness of dimensionality reduction?

Accepted Answer

Reconstruction Error

Answer

Model Complexity

Answer

Data Skewness

Answer

Feature Importance

Question 52

In a dataset with 100 features, you reduce it to 10 features using PCA. What is the potential benefit of this reduction in a classification task?

Accepted Answer

Improved model generalization by reducing overfitting

Answer

Increased computational complexity for the classifier

Answer

Reduced accuracy due to loss of information

Question 53

A company wants to analyze customer preferences based on their purchase history. Which dimensionality reduction technique would be most appropriate for visualizing customer segments based on their buying behavior?

Accepted Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Answer

Principal Component Analysis (PCA)

Question 54

What is the primary difference between feature selection and dimensionality reduction?

Accepted Answer

Feature selection discards features, while dimensionality reduction transforms them

Answer

Feature selection is more computationally expensive

Answer

Dimensionality reduction is only suitable for supervised learning