Feature Selection and Engineering MCQs With Answers

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Feature Selection and Engineering Online Quiz

By presenting 3 options to choose from, Feature Selection and Engineering Quiz which cover a wide range of topics and levels of difficulty, making them adaptable to various learning objectives and preferences. You will have to read all the given answers of Feature Selection and Engineering Questions and Answers and click over the correct answer.

• Test Name: Feature Selection and Engineering MCQ Quiz Practice
• Type: Quiz Test
• Total Questions: 40
• Total Marks: 40
• Time: 40 minutes

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Feature Selection and Engineering

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1 / 40

What is the purpose of using feature hashing?

To reduce the dimensionality of categorical features

To create interaction terms

To normalize numerical features

2 / 40

What is the purpose of scaling features?

To bring all features to a similar scale

To remove outliers

To reduce dimensionality

3 / 40

What is L2 regularization also known as?

Ridge regression

Lasso regression

Elastic Net

4 / 40

What is feature scaling?

The process of creating new features

The process of removing features

The process of bringing all features to a similar scale

5 / 40

What is the purpose of regularization in feature selection?

To reduce the size of the dataset

To handle missing values

To prevent overfitting by adding a penalty to the model

6 / 40

What is an embedded method in feature selection?

A technique that performs feature selection as part of the model training process

A technique that handles missing values

A technique that scales data

7 / 40

What is the purpose of using cross-validation in feature selection?

To reduce dimensionality

To encode categorical variables

To evaluate the performance of selected features

8 / 40

What is recursive feature elimination (RFE)?

A method to handle missing values

A feature selection technique that recursively removes the least important features

A method to scale data

9 / 40

What is the goal of feature engineering in machine learning?

To reduce the number of features

To eliminate duplicates

To create new features that enhance model performance

10 / 40

What is a benefit of using automated feature selection methods?

They can handle large datasets efficiently

They create new features automatically

They always improve model performance

11 / 40

What is a common method for feature selection?

Principal Component Analysis (PCA)

Data normalization

Recursive Feature Elimination (RFE)

12 / 40

What is the purpose of normalization in feature engineering?

To bring all features to a similar scale

To handle missing values

To remove irrelevant features

13 / 40

What is a dummy variable?

A binary variable created from a categorical variable

A variable used to replace missing values

A variable used to measure correlation

14 / 40

What is one-hot encoding?

Normalizing numerical data

Converting categorical variables into binary vectors

Reducing feature dimensionality

15 / 40

What is a common technique for handling missing values in feature engineering?

Scaling

One-hot encoding

Imputation

16 / 40

What is the difference between feature selection and feature extraction?

They are the same process

Feature selection selects existing features; feature extraction creates new features

Feature selection creates new features; feature extraction removes features

17 / 40

What is the purpose of using feature scaling techniques like Min-Max scaling and Standardization?

To remove irrelevant features

To create new features

To bring features to a similar scale

18 / 40

What is feature selection?

The process of creating new features

The process of selecting the most important variables for a model

The process of removing duplicates

19 / 40

What is a correlation matrix used for in feature selection?

To scale features

To handle missing values

To identify and remove highly correlated features

20 / 40

What is PCA used for in feature engineering?

To reduce the dimensionality of data

To scale data

To encode categorical variables

21 / 40

What is a common feature engineering technique for time series data?

Lag features

One-hot encoding

PCA

22 / 40

What is the purpose of feature selection in machine learning?

To improve model performance and reduce overfitting

To increase the number of features

To simplify data visualization

23 / 40

What is the purpose of using interaction terms in feature engineering?

To remove irrelevant features

To capture the combined effect of two or more features

To scale data

24 / 40

What is feature extraction?

The process of creating new features from existing ones

The process of normalizing data

The process of selecting important features

25 / 40

What is polynomial feature generation?

Selecting the most important features

Scaling features to a similar range

Creating new features by taking polynomial combinations of existing features

26 / 40

What is backward elimination in feature selection?

A method that starts with no features and adds the most significant ones

A method that scales features

A method that starts with all features and removes the least significant ones

27 / 40

What is the main goal of feature selection?

To increase the number of features

To improve model performance by removing irrelevant features

To simplify the dataset

28 / 40

What is feature selection bias?

The bias introduced by scaling features

The bias introduced by selecting features based on the target variable

The bias introduced by creating new features

29 / 40

What is variance thresholding?

A technique to handle missing values

A feature selection technique that removes features with low variance

A technique to scale features

30 / 40

What is the advantage of using domain knowledge in feature engineering?

It helps create more meaningful features

It reduces the computational cost

It increases the number of features

31 / 40

What is the purpose of using feature crosses in feature engineering?

To capture interactions between features

To normalize features

To reduce dimensionality

32 / 40

What is a feature importance score?

A measure of feature variability

A measure of feature correlation

A measure of how useful a feature is in predicting the target variable

33 / 40

What is a key advantage of feature selection?

It can improve model interpretability and performance

It increases the number of features

It simplifies the modeling process

34 / 40

What is L1 regularization also known as?

Lasso regression

Elastic Net

Ridge regression

35 / 40

What is forward selection in feature selection?

A method that starts with no features and adds the most significant ones

A method that normalizes data

A method that starts with all features and removes the least significant ones

36 / 40

What is a categorical feature?

A feature that represents binary values

A feature that represents continuous values

A feature that represents categories or groups

37 / 40

What is feature engineering?

The process of visualizing data

The process of creating new features from existing data

The process of removing features

38 / 40

What is the main goal of dimensionality reduction?

To increase the number of features

To eliminate duplicates

To reduce the number of features while retaining important information

39 / 40

What is mutual information used for in feature selection?

To measure the dependency between variables

To scale features

To handle missing values

40 / 40

What is a feature union?

Normalizing features to a similar range

Selecting the best features from a dataset

Combining multiple feature extraction methods into a single feature space

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