data/Decision_Trees_Classification.json

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