| { | |
| "title": "Decision Trees Mastery: 100 MCQs", | |
| "description": "A comprehensive 100-question collection to master Decision Trees — covering fundamentals, splitting criteria, pruning, overfitting control, ensemble integration, and real-world scenarios.", | |
| "questions": [ | |
| { | |
| "id": 1, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 1?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 2, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 2?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 3, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 3?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 4, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 4?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 5, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 5?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 6, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 6?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 7, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 7?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 8, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 8?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 9, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 9?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 10, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 10?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 11, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 11?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 12, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 12?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 13, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 13?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 14, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 14?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 15, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 15?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 16, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 16?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 17, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 17?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 18, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 18?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 19, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 19?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 20, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 20?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 21, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 21?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 22, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 22?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 23, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 23?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 24, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 24?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 25, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 25?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 26, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 26?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 27, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 27?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 28, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 28?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 29, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 29?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 30, | |
| "questionText": "What is the main purpose of Decision Tree in classification task 30?", | |
| "options": [ | |
| "To predict class labels", | |
| "To cluster data", | |
| "To reduce dimensions", | |
| "To normalize data" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Decision Trees are used to predict class labels based on input features." | |
| }, | |
| { | |
| "id": 31, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 32, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 33, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 34, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 35, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 36, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 37, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 38, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 39, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 40, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 41, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 42, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 43, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 44, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 45, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 46, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 47, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 48, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 49, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 50, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 51, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 52, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 53, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 54, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 55, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 56, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 57, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 58, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 59, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 60, | |
| "questionText": "Scenario: A Decision Tree is overfitting the training data. What should you do?", | |
| "options": [ | |
| "Increase tree depth", | |
| "Prune the tree", | |
| "Add more features", | |
| "Decrease learning rate" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Pruning helps reduce overfitting by removing unnecessary branches." | |
| }, | |
| { | |
| "id": 61, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 62, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 63, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 64, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 65, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 66, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 67, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 68, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 69, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 70, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 71, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 72, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 73, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 74, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 75, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 76, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 77, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 78, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 79, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 80, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 81, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 82, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 83, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 84, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 85, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 86, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 87, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 88, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 89, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 90, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 91, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 92, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 93, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 94, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 95, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 0, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 96, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 97, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 1, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 98, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 3, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 99, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| }, | |
| { | |
| "id": 100, | |
| "questionText": "Scenario: You are building a Decision Tree on a dataset with continuous features and high variance. What splitting criterion might perform best?", | |
| "options": [ | |
| "Entropy", | |
| "Information Gain", | |
| "Gini Index", | |
| "Chi-Square" | |
| ], | |
| "correctAnswerIndex": 2, | |
| "explanation": "Gini Index or Information Gain are common criteria; the best depends on data distribution, but both handle continuous attributes effectively." | |
| } | |
| ] | |
| } |