Feature Reduction Techniques Quiz

What is feature reduction?

• Process of reducing the number of input variables

• Randomly selecting input variables

• Increasing the number of input variables

• Ignoring the input variables

What are the benefits of feature reduction techniques?

• Improving model performance, reducing overfitting, and speeding up training time.

• No impact on model performance, increasing overfitting, and slowing down training time.

• Increasing model performance, reducing underfitting, and slowing down training time.

• Worsening model performance, increasing overfitting, and slowing down training time.

Explain the difference between feature selection and feature extraction.

• Feature selection focuses on selecting a subset of the original features, while feature extraction involves transforming the original features into a new set of features.

• Feature selection and feature extraction are both used for adding more features to the dataset.

• Feature selection and feature extraction are the same thing.

• Feature selection involves transforming the original features into a new set of features, while feature extraction focuses on selecting a subset of the original features.

What is the curse of dimensionality and how does feature reduction help in addressing it?

• Feature reduction has no impact on overfitting

• Curse of dimensionality is not a problem in machine learning

• Feature reduction increases the number of input variables, leading to better model performance

• Feature reduction reduces the number of input variables, improving model performance and reducing overfitting.

Discuss the principal component analysis (PCA) technique for feature reduction.

• PCA is only applicable to categorical data

• PCA is a statistical technique used to reduce the number of variables in a dataset while preserving as much information as possible.

• PCA has no impact on the dimensionality of the dataset

• PCA is a technique used to increase the number of variables in a dataset

What is the main purpose of using singular value decomposition (SVD) in feature reduction?

• SVD is primarily used to randomly select features for enhancement in a dataset

• SVD is mainly employed to reduce the dimensionality of a dataset by adding more features

• SVD is mainly utilized to decompose a matrix into singular vectors and values for feature reduction

• SVD is mainly focused on duplicating features to increase the complexity of a dataset

What is the role of L1 regularization in feature reduction?

• It reduces the model's accuracy by increasing the coefficients

• It increases the number of features by penalizing the cost function

• It adds a penalty to the cost function for the absolute value of the coefficients, leading to sparse feature selection.

• It has no impact on feature reduction

Explain the role of PCA (Principal Component Analysis) in dimensionality reduction.

• PCA is primarily used for data visualization and not for reducing dimensions

• PCA is used to increase the dimensionality of data

• PCA is not effective for linear data

• PCA is used for dimensionality reduction by transforming high-dimensional data into a lower-dimensional space while retaining the data's variance.

Explain the concept of autoencoders in feature reduction.

• Autoencoders have no role in feature reduction

• Autoencoders are not related to neural networks

• Autoencoders are used to increase the dimensionality of input data

• Autoencoders are neural networks used to reduce the dimensionality of input data by learning to encode and decode it.

What are some common challenges or limitations of feature reduction techniques?

• Increase in model performance, ease in selecting the right features, and no loss of information

• No impact on model performance, no difficulty in selecting the right features, and no decrease in model performance

• Loss of information, difficulty in selecting the right features, and potential decrease in model performance

• Gain of information, ease in selecting the right features, and potential increase in model performance