| { |
| "title": "Random Forests Mastery: 100 MCQs", |
| "description": "A 100-question comprehensive collection on Random Forests — covering bagging, ensemble voting, feature randomness, hyperparameter tuning, and real-world applications.", |
| "questions": [ |
| { |
| "id": 1, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 2, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 3, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 4, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 5, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 6, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 7, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 8, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 9, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 10, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 11, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 12, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 13, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 14, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 15, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 16, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 17, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 18, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 19, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 20, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 21, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 22, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 23, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 24, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 25, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 26, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 27, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 28, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 29, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 30, |
| "questionText": "What is a Random Forest primarily composed of?", |
| "options": [ |
| "Multiple Decision Trees", |
| "Single Neural Network", |
| "Clusters of Data Points", |
| "Gradient Functions" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "A Random Forest is an ensemble learning method that builds multiple decision trees and combines their results." |
| }, |
| { |
| "id": 31, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 32, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 33, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 34, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 35, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 36, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 37, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 38, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 39, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 40, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 41, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 42, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 43, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 44, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 45, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 46, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 47, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 48, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 49, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 50, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 51, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 52, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 53, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 54, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 55, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 56, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 57, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 58, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 59, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 60, |
| "questionText": "Scenario: Your Random Forest is overfitting the training data. What should you try first?", |
| "options": [ |
| "Reduce number of trees", |
| "Reduce tree depth or increase min_samples_split", |
| "Use smaller batch size", |
| "Add more layers" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "Reducing tree depth or increasing the minimum samples per split helps prevent overfitting by controlling tree complexity." |
| }, |
| { |
| "id": 61, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 62, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 63, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 64, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 65, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 66, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 67, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 68, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 69, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 70, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 71, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 72, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 73, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 74, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 75, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 76, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 77, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 78, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 79, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 80, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 81, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 82, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 83, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 84, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 85, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 86, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 87, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 88, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 89, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 90, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 91, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 92, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 93, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 94, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 95, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 1, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 96, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 97, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 3, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 98, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 99, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 2, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| }, |
| { |
| "id": 100, |
| "questionText": "Scenario: A Random Forest model performs poorly on unseen data despite high training accuracy. Which cause is most likely?", |
| "options": [ |
| "High bias", |
| "High variance", |
| "Low variance and low bias", |
| "Perfect generalization" |
| ], |
| "correctAnswerIndex": 0, |
| "explanation": "The model is likely suffering from high variance (overfitting), which happens when it memorizes training data too closely." |
| } |
| ] |
| } |
