NSCT-Feature Engineering & Selection MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Feature engineering is: (A) Encrypting features only (B) The process of creating new input features from existing data to improve model performance (C) Compressing datasets only (D) Backup onlyShow All Answers 2. . Feature selection is: (A) The process of choosing the most relevant features for model training (B) Encrypting selected features (C) Compressing features (D) Backup only 3. . Why is feature engineering important? (A) It helps improve model accuracy, interpretability, and efficiency (B) Encrypts features only (C) Compresses datasets (D) Backup only 4. . Common feature engineering techniques include: (A) Compressing features only (B) Encrypting variables only (C) Encoding categorical variables, scaling, creating interaction terms, and aggregations (D) Backup only 5. . One-hot encoding is used to: (A) Convert categorical variables into binary vectors (B) Encrypt categories (C) Compress data (D) Backup only 6. . Label encoding is: (A) Compressing labels (B) Encrypting labels (C) Converting categorical variables into numeric labels (D) Backup only 7. . Feature scaling is important because: (A) Encrypting scaling (B) Many algorithms like SVM, KNN, and gradient descent are sensitive to feature magnitude (C) Compressing features (D) Backup only 8. . Standardization scales features by: (A) Subtracting mean and dividing by standard deviation (B) Encrypting data (C) Compressing data (D) Backup only 9. . Normalization scales features by: (A) Compressing values (B) Encrypting values (C) Bringing values to a fixed range, often [0,1] (D) Backup only 10. . Feature interaction involves: (A) Creating new features by combining two or more existing features (B) Encrypting features (C) Compressing features (D) Backup only 11. . Principal Component Analysis (PCA) is used for: (A) Backup only (B) Encrypting components (C) Compressing features (D) Dimensionality reduction by transforming features into uncorrelated components 12. . Recursive Feature Elimination (RFE) is: (A) Encrypting features (B) A method to select important features by recursively removing less important ones (C) Compressing features (D) Backup only 13. . Mutual information in feature selection measures: (A) The dependency between features and the target variable (B) Encrypting dependency (C) Compressing data (D) Backup only 14. . Correlation-based feature selection aims to: (A) Backup only (B) Encrypt correlations (C) Compress features (D) Remove redundant features that are highly correlated with others 15. . L1 regularization (Lasso) can be used for: (A) Compressing coefficients (B) Encrypting coefficients (C) Feature selection by shrinking less important feature coefficients to zero (D) Backup only 16. . Feature importance scores from tree-based models help to: (A) Compress features (B) Encrypt features (C) Identify the most influential features for predictions (D) Backup only 17. . Removing irrelevant or noisy features helps to: (A) Encrypt features (B) Improve model performance and reduce overfitting (C) Compress datasets (D) Backup only 18. . Dimensionality reduction helps to: (A) Backup only (B) Encrypt features (C) Compress features (D) Reduce computational cost while retaining important information 19. . Interaction terms are useful when: (A) Encrypting interactions (B) Relationships between features affect the target variable (C) Compressing interactions (D) Backup only 20. . The main purpose of feature engineering and selection is to: (A) Encrypt features only (B) Prepare high-quality, relevant features to improve model accuracy, efficiency, and interpretability (C) Compress datasets only (D) Backup only
