NSCT – Supervised Learning MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Supervised learning is: (A) Training models with no data (B) A type of machine learning where the model is trained on labeled data (C) Compressing unlabeled data (D) Backup onlyShow All Answers 2. . Labeled data in supervised learning contains: (A) Only input features (B) Input features and their corresponding output or target values (C) Only outputs (D) Backup only 3. . The main goal of supervised learning is to: (A) Compress data (B) Encrypt data (C) Learn a mapping from inputs to outputs to make predictions on new data (D) Backup only 4. . Regression in supervised learning is used to: (A) Backup only (B) Predict categories (C) Encrypt numbers (D) Predict continuous numeric values 5. . Classification in supervised learning is used to: (A) Encrypt categories (B) Predict continuous values (C) Predict categorical or discrete outcomes (D) Backup only 6. . Common supervised learning algorithms include: (A) Principal component analysis only (B) K-means clustering only (C) Linear regression, logistic regression, decision trees, random forests, and support vector machines (D) Backup only 7. . Mean Squared Error (MSE) is commonly used to: (A) Backup only (B) Encrypt errors (C) Compress errors (D) Evaluate regression models 8. . Accuracy, precision, recall, and F1-score are used to: (A) Compress metrics (B) Encrypt metrics (C) Evaluate classification models (D) Backup only 9. . Training set in supervised learning is: (A) Encrypting data (B) The subset of data used to train the model (C) Compressing data (D) Backup only 10. . Test set in supervised learning is: (A) Compressing test data (B) Encrypting test data (C) The subset of data used to evaluate the model's performance on unseen data (D) Backup only 11. . Overfitting occurs when: (A) Compressing models (B) Encrypting models (C) The model performs well on training data but poorly on new data (D) Backup only 12. . Underfitting occurs when: (A) Compressing models (B) Encrypting models (C) The model is too simple to capture patterns in the data (D) Backup only 13. . Cross-validation is used to: (A) Encrypt data splits (B) Assess model performance more reliably and prevent overfitting (C) Compress validation data (D) Backup only 14. . Feature scaling in supervised learning helps: (A) Backup only (B) Encrypt features (C) Compress features (D) Improve convergence and performance of algorithms sensitive to feature magnitude 15. . Decision trees in supervised learning: (A) Split data based on feature values to make predictions (B) Encrypt trees (C) Compress trees (D) Backup only 16. . Support Vector Machines (SVM) aim to: (A) Encrypt hyperplanes (B) Find the optimal hyperplane that separates classes in the feature space (C) Compress feature spaces (D) Backup only 17. . K-Nearest Neighbors (KNN) predicts output by: (A) Considering the majority label or average value of nearest neighbors (B) Encrypting neighbors (C) Compressing neighbors (D) Backup only 18. . Regularization in supervised learning is used to: (A) Backup only (B) Encrypt coefficients (C) Compress models (D) Reduce overfitting by penalizing large coefficients 19. . Label encoding is used to: (A) Encrypt labels (B) Convert categorical variables into numeric labels for modeling (C) Compress labels (D) Backup only 20. . The main purpose of supervised learning is to: (A) Predict outputs for new inputs using a model trained on labeled data (B) Encrypt all data (C) Compress all features (D) Backup only
