Feature engineering is a crucial process in data science and machine learning that involves transforming raw data into meaningful features to improve model performance. This includes selecting, modifying, or creating new variables from the original dataset to better capture patterns, relationships, and insights. For instance, techniques like scaling numerical data, encoding categorical variables, handling missing values, and deriving new features through operations such as aggregation or polynomial transformations help models learn more effectively. By leveraging domain knowledge, feature engineering reduces noise, enhances predictive accuracy, and prevents overfitting, ultimately leading to more robust and interpretable results.
Table of contents
- Part 1: Create an amazing feature engineering quiz using AI instantly in OnlineExamMaker
- Part 2: 20 feature engineering quiz questions & answers
- Part 3: Save time and energy: generate quiz questions with AI technology
Part 1: Create an amazing feature engineering quiz using AI instantly in OnlineExamMaker
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Part 2: 20 feature engineering quiz questions & answers
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1. Question: What is the primary purpose of feature engineering in machine learning?
A. To increase the size of the dataset
B. To create new features that improve model performance
C. To visualize data patterns
D. To reduce computational resources
Answer: B
Explanation: Feature engineering transforms raw data into a format that is more suitable for machine learning algorithms, helping models learn patterns more effectively and achieve better accuracy.
2. Question: Which technique is commonly used to convert categorical variables into numerical ones?
A. Normalization
B. One-hot encoding
C. Standardization
D. Binning
Answer: B
Explanation: One-hot encoding creates binary columns for each category, allowing machine learning models to handle categorical data without assuming ordinal relationships.
3. Question: What does feature scaling aim to achieve?
A. Remove outliers from the dataset
B. Ensure all features contribute equally to the model
C. Increase the number of features
D. Convert text data into numbers
Answer: B
Explanation: Feature scaling adjusts the range of features so that they have a similar scale, preventing features with larger ranges from dominating the model during training.
4. Question: Which method is used for handling missing values in a dataset?
A. Imputation
B. Deletion
C. Both A and B
D. Neither A nor B
Answer: C
Explanation: Imputation replaces missing values with estimated ones (e.g., mean), while deletion removes rows or columns with missing data, depending on the context to maintain data integrity.
5. Question: What is the difference between label encoding and one-hot encoding?
A. Label encoding is for numerical data only
B. One-hot encoding creates more columns than label encoding
C. Label encoding is used for images
D. Both are identical
Answer: B
Explanation: Label encoding assigns a unique integer to each category, which can imply ordinality, whereas one-hot encoding expands the dataset with binary columns, avoiding any ordinal assumptions.
6. Question: In feature selection, what does the chi-squared test evaluate?
A. Correlation between features
B. Independence between features and the target variable
C. The mean of features
D. The variance of features
Answer: B
Explanation: The chi-squared test assesses the dependence between categorical features and the target, helping to select features that are most relevant for prediction.
7. Question: Which of the following is an example of a derived feature?
A. Raw data from a sensor
B. The ratio of two existing features
C. A randomly generated number
D. The original target variable
Answer: B
Explanation: Derived features, like ratios or differences, are created from existing data to capture new relationships that can enhance model performance.
8. Question: What is binning in feature engineering?
A. Grouping continuous data into categories
B. Removing duplicate entries
C. Scaling data to a range
D. Encoding text data
Answer: A
Explanation: Binning converts continuous variables into discrete bins, which can simplify the data and help models handle non-linear relationships.
9. Question: Which normalization technique scales data to a range between 0 and 1?
A. Standardization
B. Min-Max scaling
C. Z-score normalization
D. Log transformation
Answer: B
Explanation: Min-Max scaling rescales features to a fixed range, typically 0 to 1, based on the minimum and maximum values, making it useful for algorithms sensitive to data magnitude.
10. Question: How does polynomial features help in feature engineering?
A. By reducing the dataset size
B. By creating interactions between features
C. By deleting irrelevant features
D. By converting categorical data
Answer: B
Explanation: Polynomial features generate new features by raising existing ones to powers and multiplying them, capturing non-linear relationships in the data.
11. Question: What is the role of TF-IDF in text feature engineering?
A. To count word frequencies
B. To weigh word importance in a document relative to a corpus
C. To remove stop words
D. To translate text into numbers
Answer: B
Explanation: TF-IDF assigns weights to words based on their frequency in a document and rarity in the entire corpus, highlighting significant terms for text analysis.
12. Question: Which feature engineering technique is useful for time series data?
A. Lag features
B. One-hot encoding
C. Normalization
D. All of the above
Answer: A
Explanation: Lag features incorporate past values of a time series as inputs, helping models capture temporal dependencies and patterns.
13. Question: In feature engineering, what does correlation analysis help with?
A. Identifying multicollinearity between features
B. Predicting the target variable
C. Visualizing data distributions
D. Encoding categorical variables
Answer: A
Explanation: Correlation analysis detects highly correlated features, which can lead to multicollinearity, allowing engineers to remove redundant features for better model stability.
14. Question: What is the purpose of feature extraction in high-dimensional data?
A. To increase dimensionality
B. To reduce the number of features while retaining information
C. To add noise to the data
D. To encode labels
Answer: B
Explanation: Feature extraction techniques, like PCA, transform data into a lower-dimensional space, reducing complexity without losing critical information.
15. Question: Which method is best for handling imbalanced datasets in feature engineering?
A. Oversampling the minority class
B. Creating synthetic features
C. Both A and techniques like SMOTE
D. Ignoring the imbalance
Answer: C
Explanation: Oversampling or synthetic methods like SMOTE generate additional samples for the minority class, ensuring features are balanced to improve model generalization.
16. Question: What is domain knowledge’s role in feature engineering?
A. It has no role
B. It guides the creation of meaningful features
C. It is only used for data cleaning
D. It replaces machine learning algorithms
Answer: B
Explanation: Domain knowledge helps identify relevant features and transformations based on real-world context, leading to more effective and interpretable models.
17. Question: How does log transformation aid feature engineering?
A. By making data more linear
B. By increasing skewness
C. By removing categorical variables
D. By standardizing data
Answer: A
Explanation: Log transformation stabilizes variance and makes skewed data more normally distributed, which is beneficial for linear models and improving feature relationships.
18. Question: What is recursive feature elimination (RFE)?
A. A method to add features recursively
B. A technique to remove features based on model performance
C. A way to encode data
D. A visualization tool
Answer: B
Explanation: RFE uses a model to iteratively remove the least important features, selecting an optimal subset that maximizes predictive power.
19. Question: Which of the following is a disadvantage of one-hot encoding?
A. It increases dataset size significantly
B. It only works for numerical data
C. It removes all categories
D. It is not used in machine learning
Answer: A
Explanation: One-hot encoding can lead to a high-dimensional dataset, potentially causing the curse of dimensionality and increased computational costs.
20. Question: In feature engineering, why is feature importance analysis useful?
A. To complicate the model
B. To identify and prioritize key features for better predictions
C. To add random features
D. To ignore the target variable
Answer: B
Explanation: Feature importance analysis, often from models like Random Forests, helps engineers focus on the most influential features, streamlining the dataset and improving efficiency.
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