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 crosses in feature engineering?

To capture interactions between features

To normalize features

To reduce dimensionality

2 / 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

3 / 40

What is the purpose of feature selection in machine learning?

To increase the number of features

To improve model performance and reduce overfitting

To simplify data visualization

4 / 40

What is the main goal of dimensionality reduction?

To eliminate duplicates

To reduce the number of features while retaining important information

To increase the number of features

5 / 40

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

To reduce dimensionality

To evaluate the performance of selected features

To encode categorical variables

6 / 40

What is one-hot encoding?

Normalizing numerical data

Reducing feature dimensionality

Converting categorical variables into binary vectors

7 / 40

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

To create new features

To remove irrelevant features

To bring features to a similar scale

8 / 40

What is a categorical feature?

A feature that represents continuous values

A feature that represents binary values

A feature that represents categories or groups

9 / 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

10 / 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

11 / 40

What is a correlation matrix used for in feature selection?

To identify and remove highly correlated features

To scale features

To handle missing values

12 / 40

What is feature selection bias?

The bias introduced by creating new features

The bias introduced by scaling features

The bias introduced by selecting features based on the target variable

13 / 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

14 / 40

What is an embedded method in feature selection?

A technique that scales data

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

A technique that handles missing values

15 / 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

16 / 40

What is a dummy variable?

A variable used to replace missing values

A variable used to measure correlation

A binary variable created from a categorical variable

17 / 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

18 / 40

What is the purpose of scaling features?

To reduce dimensionality

To remove outliers

To bring all features to a similar scale

19 / 40

What is the difference between feature selection and feature extraction?

They are the same process

Feature selection creates new features; feature extraction removes features

Feature selection selects existing features; feature extraction creates new features

20 / 40

What is feature extraction?

The process of normalizing data

The process of creating new features from existing ones

The process of selecting important features

21 / 40

What is PCA used for in feature engineering?

To scale data

To encode categorical variables

To reduce the dimensionality of data

22 / 40

What is a common method for feature selection?

Data normalization

Recursive Feature Elimination (RFE)

Principal Component Analysis (PCA)

23 / 40

What is the purpose of using feature hashing?

To create interaction terms

To reduce the dimensionality of categorical features

To normalize numerical features

24 / 40

What is the purpose of regularization in feature selection?

To reduce the size of the dataset

To prevent overfitting by adding a penalty to the model

To handle missing values

25 / 40

What is mutual information used for in feature selection?

To handle missing values

To scale features

To measure the dependency between variables

26 / 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

27 / 40

What is variance thresholding?

A technique to handle missing values

A technique to scale features

A feature selection technique that removes features with low variance

28 / 40

What is feature selection?

The process of selecting the most important variables for a model

The process of removing duplicates

The process of creating new features

29 / 40

What is L1 regularization also known as?

Lasso regression

Elastic Net

Ridge regression

30 / 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

31 / 40

What is the purpose of normalization in feature engineering?

To handle missing values

To bring all features to a similar scale

To remove irrelevant features

32 / 40

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

One-hot encoding

Scaling

Imputation

33 / 40

What is L2 regularization also known as?

Lasso regression

Ridge regression

Elastic Net

34 / 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

35 / 40

What is a feature union?

Combining multiple feature extraction methods into a single feature space

Normalizing features to a similar range

Selecting the best features from a dataset

36 / 40

What is recursive feature elimination (RFE)?

A feature selection technique that recursively removes the least important features

A method to scale data

A method to handle missing values

37 / 40

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

Lag features

One-hot encoding

PCA

38 / 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

39 / 40

What is feature engineering?

The process of creating new features from existing data

The process of removing features

The process of visualizing data

40 / 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

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