{
  "title": "XGBoost Regression Mastery: 100 MCQs",
  "description": "A complete set of 100 multiple-choice questions covering XGBoost Regression \u2014 from basics to advanced tuning and scenario-based problem solving.",
  "questions": [
    {
      "id": 1,
      "questionText": "What does XGBoost primarily stand for in machine learning?",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 3,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 2,
      "questionText": "What does XGBoost primarily stand for in machine learning?",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 3,
      "questionText": "What does XGBoost primarily stand for in machine learning?",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 4,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q4)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 5,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q5)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 3,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 6,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q6)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 7,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q7)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 8,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q8)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 9,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q9)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 10,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q10)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 1,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 11,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q11)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 12,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q12)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 3,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 13,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q13)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 3,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 14,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q14)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 1,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 15,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q15)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 1,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 16,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q16)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 3,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 17,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q17)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 18,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q18)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 19,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q19)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 3,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 20,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q20)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 21,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q21)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 22,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q22)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 23,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q23)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 3,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 24,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q24)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 3,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 25,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q25)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 1,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 26,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q26)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 27,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q27)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 28,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q28)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 1,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 29,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q29)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 30,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q30)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 31,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q31)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 1,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 32,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q32)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 33,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q33)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 34,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q34)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 35,
      "questionText": "What does XGBoost primarily stand for in machine learning (Easy Q35)",
      "options": [
        "Extreme Gradient Boosting",
        "Extra Gaussian Boost",
        "Extended Gradient Binary Output",
        "Exponential Gain Booster"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost stands for Extreme Gradient Boosting, a highly efficient and scalable implementation of gradient boosted decision trees."
    },
    {
      "id": 36,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q36)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 1,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 37,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q37)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 38,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q38)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 39,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q39)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 40,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q40)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 41,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q41)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 42,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q42)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 43,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q43)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 44,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q44)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 1,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 45,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q45)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 46,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q46)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 47,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q47)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 1,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 48,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q48)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 49,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q49)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 50,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q50)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 51,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q51)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 52,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q52)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 53,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q53)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 54,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q54)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 55,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q55)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 56,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q56)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 57,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q57)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 1,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 58,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q58)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 1,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 59,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q59)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 1,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 60,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q60)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 61,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q61)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 62,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q62)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 63,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q63)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 64,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q64)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 65,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q65)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 66,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q66)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 0,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 67,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q67)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 68,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q68)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 1,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 69,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q69)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 70,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q70)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 71,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q71)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 72,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q72)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 2,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 73,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q73)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 1,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 74,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q74)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 75,
      "questionText": "In XGBoost Regression, what is the purpose of the learning rate (eta) (Medium Q75)",
      "options": [
        "It controls the depth of trees.",
        "It prevents overfitting by reducing feature importance.",
        "It scales the contribution of each tree during training.",
        "It increases the randomness in sampling data."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The learning rate (eta) determines how much each new tree contributes to the overall model. Smaller values make learning slower but often improve generalization."
    },
    {
      "id": 76,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q76)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 77,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q77)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 78,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q78)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 79,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q79)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 80,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q80)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 81,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q81)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 82,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q82)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 83,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q83)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 84,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q84)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 85,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q85)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 86,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q86)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 87,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q87)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 88,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q88)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 89,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q89)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 90,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q90)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 91,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q91)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 92,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q92)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 93,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q93)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 94,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q94)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 95,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q95)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 96,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q96)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 97,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q97)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 98,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q98)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 99,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q99)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    },
    {
      "id": 100,
      "questionText": "Scenario: A data scientist is using XGBoost Regression to predict house prices. She observes the model overfits despite tuning 'max_depth' and 'learning_rate'. Which additional parameter might help reduce overfitting (Hard Q100)",
      "options": [
        "Increase subsample and colsample_bytree values",
        "Reduce regularization parameters lambda and alpha",
        "Add more trees to the ensemble",
        "Set booster to 'gblinear'"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing subsample and colsample_bytree introduces randomness by sampling data and features for each tree, helping prevent overfitting."
    }
  ]
}