50 Must-Know Feature Engineering Interview Questions and Answers 2024

Feature Engineering Fundamentals

1.
What is feature engineering and how does it impact the performance of machine learning models?
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2.
List different types of features commonly used in machine learning.
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3.
Explain the differences between feature selection and feature extraction.
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4.
What are some common challenges you might face when engineering features?
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5.
Describe the process of feature normalization and standardization.
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6.
Why is it important to understand the domain knowledge while performing feature engineering?
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7.
How does feature scaling affect the performance of gradient descent?
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8.
Explain the concept of one-hot encoding and when you might use it.
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9.
What is dimensionality reduction and how can it be beneficial in machine learning?
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10.
How do you handle categorical variables in a dataset?
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Feature Selection Techniques

11.
What are filter methods in feature selection and when are they used?
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12.
Explain what wrapper methods are in the context of feature selection.
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13.
Describe embedded methods for feature selection and their benefits.
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14.
How does a feature’s correlation with the target variable influence feature selection?
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15.
What is the purpose of using Recursive Feature Elimination (RFE)?
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16.
Discuss how Random Forest can be used for feature importance estimation.
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17.
What is Regularization and how does it perform feature selection implicitly?
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18.
How can you use mutual information to select relevant features?
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Feature Extraction Methods

19.
Explain Principal Component Analysis (PCA) and its role in feature engineering.
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20.
Describe how Autoencoders can be used for feature extraction.
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21.
What is t-distributed Stochastic Neighbor Embedding (t-SNE) and how is it useful?
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22.
Explain the use of Linear Discriminant Analysis (LDA) in feature extraction.
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23.
Discuss the concept of Independent Component Analysis (ICA).
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Practical Implementation and Challenges

24.
How do you deal with missing values during feature engineering?
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25.
Describe methods for dealing with imbalanced datasets when engineering features.
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26.
How do you detect and treat outliers during the feature engineering process?
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27.
Discuss the trade-off between adding more features and the risk of overfitting.
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28.
How do you evaluate the effectiveness of your engineered features?
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29.
What is the curse of dimensionality and how can feature engineering address it?
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30.
How do you handle time-series data in feature engineering?
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Advanced Feature Engineering Techniques

31.
How can you use domain knowledge to create interaction features?
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32.
Explain the concept of polynomial feature expansion.
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33.
What are some feature engineering techniques specific to text data?
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34.
Describe how deep learning can be used for automatic feature engineering.
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35.
Can you use genetic algorithms for feature engineering? If yes, how?
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Coding Challenges

36.
Write a Python function that normalizes a given feature vector.
Answer:
37.
Code a function in Python for one-hot encoding categorical variables.
Answer:
38.
Implement PCA from scratch in Python and apply it to a sample dataset.
Answer:
39.
Create a Python script that uses scikit-learn’s SelectKBest class to select the top ‘k’ features based on a chosen statistical test.
Answer:
40.
Design a Python function to handle missing data by imputing with mean, median, or mode.
Answer:
41.
Write an SQL command to create new feature columns based on existing data.
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42.
Implement a basic feature hashing scheme in Python.
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43.
Create a Python script to detect and remove highly correlated features from a dataset.
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44.
Write a Python function that generates polynomial and interaction features using scikit-learn’s PolynomialFeatures.
Answer:
45.
Implement feature scaling (both normalization and standardization) in Python using NumPy.
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Case Studies and Scenario-Based Questions

46.
Explain how you would perform feature engineering for a recommendation system.
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47.
Discuss your approach to feature engineering for a predictive maintenance problem.
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48.
Describe the feature engineering process you would use for a customer churn prediction model.
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49.
How would you approach feature engineering for real-time anomaly detection in web traffic data?
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50.
Propose a strategy for feature engineering in a natural language processing task to predict sentiment.
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50 Feature Engineering interview questions

What is feature engineering and how does it impact the performance of machine learning models?

List different types of features commonly used in machine learning.

Explain the differences between feature selection and feature extraction.

What are some common challenges you might face when engineering features?

Describe the process of feature normalization and standardization.

Why is it important to understand the domain knowledge while performing feature engineering?

How does feature scaling affect the performance of gradient descent?

Explain the concept of one-hot encoding and when you might use it.

What is dimensionality reduction and how can it be beneficial in machine learning?

How do you handle categorical variables in a dataset?

What are filter methods in feature selection and when are they used?

Explain what wrapper methods are in the context of feature selection.

Describe embedded methods for feature selection and their benefits.

How does a feature’s correlation with the target variable influence feature selection?

What is the purpose of using Recursive Feature Elimination (RFE)?

Discuss how Random Forest can be used for feature importance estimation.

What is Regularization and how does it perform feature selection implicitly?

How can you use mutual information to select relevant features?

Explain Principal Component Analysis (PCA) and its role in feature engineering.

Describe how Autoencoders can be used for feature extraction.

What is t-distributed Stochastic Neighbor Embedding (t-SNE) and how is it useful?

Explain the use of Linear Discriminant Analysis (LDA) in feature extraction.

Discuss the concept of Independent Component Analysis (ICA).

How do you deal with missing values during feature engineering?

Describe methods for dealing with imbalanced datasets when engineering features.

How do you detect and treat outliers during the feature engineering process?

Discuss the trade-off between adding more features and the risk of overfitting.

How do you evaluate the effectiveness of your engineered features?

What is the curse of dimensionality and how can feature engineering address it?

How do you handle time-series data in feature engineering?

How can you use domain knowledge to create interaction features?

Explain the concept of polynomial feature expansion.

What are some feature engineering techniques specific to text data?

Describe how deep learning can be used for automatic feature engineering.

Can you use genetic algorithms for feature engineering? If yes, how?

Write a Python function that normalizes a given feature vector.

Code a function in Python for one-hot encoding categorical variables.

Implement PCA from scratch in Python and apply it to a sample dataset.

Create a Python script that uses scikit-learn’s SelectKBest class to select the top ‘k’ features based on a chosen statistical test.

Design a Python function to handle missing data by imputing with mean, median, or mode.

Write an SQL command to create new feature columns based on existing data.

Implement a basic feature hashing scheme in Python.

Create a Python script to detect and remove highly correlated features from a dataset.

Write a Python function that generates polynomial and interaction features using scikit-learn’s PolynomialFeatures.

Implement feature scaling (both normalization and standardization) in Python using NumPy.

Explain how you would perform feature engineering for a recommendation system.

Discuss your approach to feature engineering for a predictive maintenance problem.

Describe the feature engineering process you would use for a customer churn prediction model.

How would you approach feature engineering for real-time anomaly detection in web traffic data?

Propose a strategy for feature engineering in a natural language processing task to predict sentiment.

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