Dimensionality Reduction: A Comprehensive Guide for Machine Learning

Question 1

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

Accepted Answer

Increased data sparsity

Answer

Enhanced computational efficiency

Answer

Improved data visualization

Answer

Reduced overfitting

Question 2

Which technique is NOT designed for dimensionality reduction in machine learning?

Accepted Answer

Cluster Analysis

Answer

Principal Component Analysis

Answer

Linear Discriminant Analysis

Answer

Multidimensional Scaling

Question 3

Which of the following is a commonly used dimensionality reduction technique?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Decision Tree

Answer

Regression Analysis

Answer

Clustering

Question 4

What is the primary goal of employing dimensionality reduction?

Accepted Answer

To reduce the number of features while preserving essential information

Answer

To increase the accuracy of a machine learning model

Answer

To remove redundant features

Question 5

When using PCA, which components should be retained?

Accepted Answer

Components with higher eigenvalues

Answer

No components

Answer

Components with lower eigenvalues

Answer

All components

Question 6

Which type of dimensionality reduction technique is Linear Discriminant Analysis (LDA)?

Accepted Answer

Linear

Answer

Nonlinear

Answer

Manifold

Question 7

Which hyperparameter can be adjusted in PCA?

Accepted Answer

Number of components

Answer

Epoch

Answer

Kernel function

Answer

Learning rate

Question 8

How is the dimensionality of the reduced data determined in LDA?

Accepted Answer

By the number of classes in the dataset

Answer

By the number of features in the original data

Answer

By a user-defined parameter

Answer

By the number of samples in the dataset

Question 9

Which of the following techniques is not considered dimensionality reduction?

Accepted Answer

Clustering

Answer

Linear Discriminant Analysis (LDA)

Answer

Principal Component Analysis (PCA)

Answer

Factor Analysis

Question 10

Which dimensionality reduction technique generates new uncorrelated features by maximizing variance?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Linear Discriminant Analysis (LDA)

Answer

Factor Analysis

Question 11

Which dimensionality reduction technique is effective in separating different data classes?

Accepted Answer

Linear Discriminant Analysis (LDA)

Answer

Independent Component Analysis (ICA)

Answer

Factor Analysis

Answer

Principal Component Analysis (PCA)

Question 12

When selecting a dimensionality reduction technique, which factor is not typically considered?

Accepted Answer

Data sparsity

Answer

Number of features

Answer

Data distribution

Answer

Computational resources

Question 13

A potential disadvantage of using dimensionality reduction in machine learning is:

Accepted Answer

Potential loss of relevant information during feature elimination

Answer

Increased computational complexity

Answer

Reduction in model accuracy

Answer

Difficulty visualizing high-dimensional data

Question 14

Which of the following accurately describes the primary objective of dimensionality reduction in machine learning?

Accepted Answer

To reduce the number of features in a dataset while preserving its essential information

Answer

To increase the accuracy of a machine learning model without altering the number of features

Answer

To speed up the training process of a machine learning model by reducing the feature count

Question 15

Which dimensionality reduction technique is commonly used for linear transformations, preserving the variance in the data?

Accepted Answer

Principal Component Analysis (PCA)

Answer

t-SNE

Answer

Autoencoders

Answer

k-Means Clustering

Question 16

Which dimensionality reduction technique is well-suited for handling non-linear relationships within data?

Accepted Answer

t-SNE

Answer

PCA

Answer

k-Means Clustering

Question 17

Which of the following is a potential drawback of dimensionality reduction?

Accepted Answer

Loss of important information during feature reduction

Answer

Enhanced data visualization capabilities for better human understanding

Answer

Increased model performance due to reduced computational cost

Answer

Improved model interpretability due to fewer features

Question 18

Which dimensionality reduction technique is most suitable for preserving the variance of the data while reducing dimensionality?

Accepted Answer

PCA

Answer

Feature Selection

Answer

LDA

Answer

t-SNE

Question 19

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

Accepted Answer

To reduce the number of features in a dataset while retaining important information

Answer

To generate new features from existing ones

Answer

To increase the complexity of models by adding new features

Question 20

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

Accepted Answer

Increased model complexity, making it harder to interpret

Answer

Improved model performance due to reduced overfitting

Answer

Reduced training time for machine learning models

Answer

Enhanced data visualization for easier human comprehension

Question 21

A dataset initially has 10 features. After applying PCA, the number of features is reduced to 5. What is the new dimensionality of the dataset?

Accepted Answer

5

Answer

10

Answer

15

Answer

2

Question 22

Consider a dataset containing images of handwritten digits. Which dimensionality reduction technique would be most appropriate for visualizing the data effectively?

Accepted Answer

t-SNE

Answer

PCA

Answer

LDA

Question 23

What is the key distinction between Feature Selection and Feature Extraction in dimensionality reduction?

Accepted Answer

Feature Selection eliminates existing features, while Feature Extraction generates new features.

Answer

Feature Selection is primarily used for unsupervised learning, while Feature Extraction is suited for supervised learning.

Answer

Feature Selection focuses on preserving data variance, while Feature Extraction prioritizes class separation.

Question 24

Which dimensionality reduction technique is commonly employed for classification tasks, especially when dealing with high-dimensional data with a limited number of classes?

Accepted Answer

Linear Discriminant Analysis (LDA)

Answer

Principal Component Analysis (PCA)

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Autoencoders

Question 25

Which of the following is NOT a primary objective of dimensionality reduction?

Accepted Answer

Increasing the model's complexity

Answer

Simplifying data visualization

Answer

Reducing computational cost

Answer

Improving model performance

Question 26

Which technique is most suitable for reducing the dimensionality of a dataset with a high degree of correlation between features?

Accepted Answer

Principal Component Analysis (PCA)

Answer

t-SNE

Answer

Feature selection

Answer

Linear Discriminant Analysis (LDA)

Question 27

What is the primary advantage of using Linear Discriminant Analysis (LDA) over PCA for dimensionality reduction?

Accepted Answer

LDA incorporates class labels, making it more effective for classification tasks.

Answer

LDA is computationally less demanding than PCA.

Answer

LDA preserves more variance in the data than PCA.

Question 28

You are working with a dataset containing 100 features and aim to reduce the dimensionality to 10 features while preserving as much variance as possible. Which technique would you likely choose?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Feature selection using L1 regularization

Answer

Autoencoders

Answer

t-SNE

Question 29

In scikit-learn's PCA implementation, what is the purpose of the 'n_components' parameter?

Accepted Answer

Specifies the number of principal components to keep.

Answer

Sets the maximum number of iterations for the algorithm.

Answer

Determines the threshold for variance explained.

Question 30

Which of the following is a common metric used to assess the performance of dimensionality reduction techniques?

Accepted Answer

Reconstruction error

Answer

F1-score

Answer

AUC

Answer

Accuracy

Question 31

After applying PCA to a dataset, you observe that the first two principal components account for 95% of the variance. What does this imply?

Accepted Answer

The original data can be effectively represented in a 2-dimensional space.

Answer

The dataset is linearly separable.

Answer

The dataset is highly correlated.

Question 32

In which scenario would you likely use feature selection instead of dimensionality reduction techniques like PCA?

Accepted Answer

When the dataset contains a large number of irrelevant features and a small number of significant features.

Answer

When the data is highly correlated.

Answer

When you want to preserve the original feature space.

Question 33

Which of the following statements about autoencoders is TRUE?

Accepted Answer

Autoencoders can be used for dimensionality reduction by learning a compressed representation of the data.

Answer

Autoencoders are strictly linear dimensionality reduction techniques.