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 L2 regularization also known as?

Ridge regression

Lasso regression

Elastic Net

2 / 40

What is a correlation matrix used for in feature selection?

To handle missing values

To identify and remove highly correlated features

To scale features

3 / 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

4 / 40

What is a feature union?

Combining multiple feature extraction methods into a single feature space

Selecting the best features from a dataset

Normalizing features to a similar range

5 / 40

What is feature engineering?

The process of creating new features from existing data

The process of visualizing data

The process of removing features

6 / 40

What is the purpose of normalization in feature engineering?

To remove irrelevant features

To bring all features to a similar scale

To handle missing values

7 / 40

What is one-hot encoding?

Normalizing numerical data

Converting categorical variables into binary vectors

Reducing feature dimensionality

8 / 40

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

One-hot encoding

Imputation

Scaling

9 / 40

What is feature scaling?

The process of bringing all features to a similar scale

The process of removing features

The process of creating new features

10 / 40

What is backward elimination in feature selection?

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

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

A method that scales features

11 / 40

What is a key advantage of feature selection?

It simplifies the modeling process

It can improve model interpretability and performance

It increases the number of features

12 / 40

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

To evaluate the performance of selected features

To reduce dimensionality

To encode categorical variables

13 / 40

What is the main goal of feature selection?

To simplify the dataset

To improve model performance by removing irrelevant features

To increase the number of features

14 / 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

15 / 40

What is feature selection bias?

The bias introduced by selecting features based on the target variable

The bias introduced by creating new features

The bias introduced by scaling features

16 / 40

What is polynomial feature generation?

Creating new features by taking polynomial combinations of existing features

Scaling features to a similar range

Selecting the most important features

17 / 40

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

To normalize features

To reduce dimensionality

To capture interactions between features

18 / 40

What is PCA used for in feature engineering?

To encode categorical variables

To scale data

To reduce the dimensionality of data

19 / 40

What is the purpose of using feature hashing?

To create interaction terms

To reduce the dimensionality of categorical features

To normalize numerical features

20 / 40

What is the purpose of regularization in feature selection?

To prevent overfitting by adding a penalty to the model

To handle missing values

To reduce the size of the dataset

21 / 40

What is the main goal of dimensionality reduction?

To eliminate duplicates

To increase the number of features

To reduce the number of features while retaining important information

22 / 40

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

Lag features

One-hot encoding

PCA

23 / 40

What is a common method for feature selection?

Data normalization

Recursive Feature Elimination (RFE)

Principal Component Analysis (PCA)

24 / 40

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

It reduces the computational cost

It helps create more meaningful features

It increases the number of features

25 / 40

What is feature selection?

The process of removing duplicates

The process of selecting the most important variables for a model

The process of creating new features

26 / 40

What is the purpose of scaling features?

To remove outliers

To reduce dimensionality

To bring all features to a similar scale

27 / 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

28 / 40

What is the purpose of feature selection in machine learning?

To simplify data visualization

To increase the number of features

To improve model performance and reduce overfitting

29 / 40

What is a feature importance score?

A measure of feature correlation

A measure of feature variability

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

30 / 40

What is an embedded method in feature selection?

A technique that scales data

A technique that handles missing values

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

31 / 40

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

To create new features

To bring features to a similar scale

To remove irrelevant features

32 / 40

What is feature extraction?

The process of selecting important features

The process of creating new features from existing ones

The process of normalizing data

33 / 40

What is a categorical feature?

A feature that represents continuous values

A feature that represents categories or groups

A feature that represents binary values

34 / 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

35 / 40

What is the goal of feature engineering in machine learning?

To eliminate duplicates

To reduce the number of features

To create new features that enhance model performance

36 / 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

37 / 40

What is L1 regularization also known as?

Ridge regression

Lasso regression

Elastic Net

38 / 40

What is recursive feature elimination (RFE)?

A method to scale data

A feature selection technique that recursively removes the least important features

A method to handle missing values

39 / 40

What is mutual information used for in feature selection?

To handle missing values

To measure the dependency between variables

To scale features

40 / 40

What is the difference between feature selection and feature extraction?

Feature selection creates new features; feature extraction removes features

Feature selection selects existing features; feature extraction creates new features

They are the same process

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