{
  "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."
    }
  ]
}