data/Boosting.json

{
  "title": "Boosting Mastery: 100 MCQs",
  "description": "A comprehensive set of 100 multiple-choice questions designed to test and deepen your understanding of Boosting, from basic concepts to advanced applications including AdaBoost, Gradient Boosting, XGBoost, and real-world scenarios.",
  "questions": [
    {
      "id": 1,
      "questionText": "What is the main goal of Boosting?",
      "options": [
        "Reduce dataset size",
        "Reduce bias and improve predictive accuracy",
        "Reduce variance only",
        "Cluster similar instances"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Boosting is an ensemble technique that sequentially combines weak learners to reduce bias and improve predictive accuracy by focusing on errors made by previous models."
    },
    {
      "id": 2,
      "questionText": "Which characteristic defines a weak learner in Boosting?",
      "options": [
        "Unsupervised algorithm",
        "Perfect prediction capability",
        "Slightly better than random guessing",
        "High-variance model"
      ],
      "correctAnswerIndex": 2,
      "explanation": "A weak learner performs slightly better than random chance. Boosting combines many such weak learners to create a strong model."
    },
    {
      "id": 3,
      "questionText": "How does Boosting handle misclassified samples?",
      "options": [
        "It reduces their weights",
        "It removes them from the dataset",
        "It ignores misclassified samples",
        "It increases their weights for the next learner"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Boosting assigns higher weights to misclassified samples so that subsequent models focus on them, improving overall accuracy."
    },
    {
      "id": 4,
      "questionText": "Which of the following is a common Boosting algorithm?",
      "options": [
        "PCA",
        "Random Forest",
        "K-Means",
        "AdaBoost"
      ],
      "correctAnswerIndex": 3,
      "explanation": "AdaBoost is one of the earliest and most common Boosting algorithms, combining weak learners sequentially."
    },
    {
      "id": 5,
      "questionText": "Boosting is generally considered:",
      "options": [
        "Unsupervised technique",
        "Sequential ensemble method",
        "Parallel ensemble method",
        "Clustering algorithm"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Boosting trains models sequentially, each focusing on the errors of the previous model, unlike Bagging which is parallel."
    },
    {
      "id": 6,
      "questionText": "In AdaBoost, what does the weight assigned to each weak learner represent?",
      "options": [
        "Its contribution to the final model",
        "Its bias only",
        "Its variance",
        "Its training time"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Each weak learner is assigned a weight based on its accuracy. More accurate learners have higher influence in the final ensemble."
    },
    {
      "id": 7,
      "questionText": "Which error type does Boosting primarily aim to reduce?",
      "options": [
        "Bias",
        "Dataset error",
        "Irreducible error",
        "Variance"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Boosting sequentially trains weak learners to correct previous errors, reducing bias and improving model accuracy."
    },
    {
      "id": 8,
      "questionText": "What is the key difference between Bagging and Boosting?",
      "options": [
        "Boosting always uses deep learners",
        "Bagging reduces bias; Boosting reduces variance",
        "Bagging trains models independently; Boosting sequentially",
        "Bagging requires weighted samples; Boosting does not"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Bagging reduces variance by averaging independent models. Boosting reduces bias by sequentially training learners focusing on previous errors."
    },
    {
      "id": 9,
      "questionText": "Gradient Boosting differs from AdaBoost because it:",
      "options": [
        "Is unsupervised",
        "Uses parallel trees",
        "Optimizes a loss function using gradient descent",
        "Ignores misclassified samples"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Gradient Boosting sequentially fits models to the residuals of the previous model using gradient descent to optimize a chosen loss function."
    },
    {
      "id": 10,
      "questionText": "Which metric can be used to evaluate Boosting performance for classification?",
      "options": [
        "Accuracy, F1-score, AUC",
        "Silhouette Score",
        "Mean Squared Error only",
        "R-squared"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Classification metrics such as Accuracy, F1-score, and AUC are suitable for evaluating Boosting performance on classification tasks."
    },
    {
      "id": 11,
      "questionText": "Boosting works best with:",
      "options": [
        "High bias weak learners",
        "Clustering algorithms",
        "Unsupervised learners",
        "Low bias, low variance models"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Boosting combines weak learners that are biased but not too complex, sequentially correcting errors to form a strong model."
    },
    {
      "id": 12,
      "questionText": "What is the role of learning rate in Boosting?",
      "options": [
        "Reduces number of features",
        "Controls contribution of each weak learner",
        "Controls bootstrap sample size",
        "Determines tree depth"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Learning rate scales the contribution of each weak learner, allowing fine-tuning of the ensemble to prevent overfitting."
    },
    {
      "id": 13,
      "questionText": "Which of these is true about overfitting in Boosting?",
      "options": [
        "Boosting is only for regression",
        "Boosting always underfits",
        "Boosting can overfit if too many weak learners are used",
        "Boosting never overfits"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Using too many learners or too complex learners can lead Boosting to overfit, especially with small datasets."
    },
    {
      "id": 14,
      "questionText": "XGBoost differs from standard Gradient Boosting in that it:",
      "options": [
        "Ignores gradients",
        "Is unsupervised",
        "Does not use trees",
        "Uses regularization and optimized computation"
      ],
      "correctAnswerIndex": 3,
      "explanation": "XGBoost adds regularization (L1 and L2) and efficient computational techniques, improving performance and reducing overfitting."
    },
    {
      "id": 15,
      "questionText": "What is the main advantage of Boosting over a single model?",
      "options": [
        "Higher accuracy and lower bias",
        "Faster training",
        "Simplified model interpretation",
        "Reduced number of features"
      ],
      "correctAnswerIndex": 0,
      "explanation": "By sequentially correcting errors, Boosting often achieves higher accuracy and reduces bias compared to a single model."
    },
    {
      "id": 16,
      "questionText": "How does Boosting handle misclassified instances in regression?",
      "options": [
        "Ignores residuals",
        "Uses majority voting",
        "Focuses on residuals for next learner",
        "Removes outliers completely"
      ],
      "correctAnswerIndex": 2,
      "explanation": "In regression, Boosting fits the next weak learner to the residuals (errors) of the previous learner, reducing bias."
    },
    {
      "id": 17,
      "questionText": "Which of the following is NOT a Boosting algorithm?",
      "options": [
        "Gradient Boosting",
        "Random Forest",
        "AdaBoost",
        "XGBoost"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Random Forest is a Bagging-based ensemble method, not Boosting."
    },
    {
      "id": 18,
      "questionText": "Boosting is most useful when:",
      "options": [
        "Weak learners have high bias",
        "Clustering is needed",
        "Base learners have low variance",
        "Dataset is extremely large"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Boosting reduces bias by combining weak learners that perform slightly better than chance."
    },
    {
      "id": 19,
      "questionText": "Which is true about sequential learning in Boosting?",
      "options": [
        "Bootstrap samples are ignored",
        "Each model depends on previous model’s errors",
        "Models are trained independently",
        "Training is unsupervised"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Boosting trains models sequentially, with each learner focusing on the errors of previous learners to reduce bias."
    },
    {
      "id": 20,
      "questionText": "Gradient Boosting can be used with which loss functions?",
      "options": [
        "Only log-loss",
        "Only hinge loss",
        "Only squared error",
        "Any differentiable loss function"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Gradient Boosting is flexible and can optimize any differentiable loss function appropriate for the problem."
    },
    {
      "id": 21,
      "questionText": "Boosting can handle overfitting better with:",
      "options": [
        "Higher learning rate",
        "Lower learning rate and early stopping",
        "Ignoring residuals",
        "More features only"
      ],
      "correctAnswerIndex": 1,
      "explanation": "A lower learning rate reduces the contribution of each learner, and early stopping prevents adding too many learners, mitigating overfitting."
    },
    {
      "id": 22,
      "questionText": "Which property makes Boosting different from Bagging?",
      "options": [
        "Bootstrap sampling only",
        "Random feature selection",
        "Sequential error correction",
        "Parallel variance reduction"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Boosting sequentially corrects errors, whereas Bagging builds independent models in parallel for variance reduction."
    },
    {
      "id": 23,
      "questionText": "AdaBoost works primarily with which type of learners?",
      "options": [
        "Decision stumps",
        "Linear regression",
        "Deep neural networks",
        "Clustering models"
      ],
      "correctAnswerIndex": 0,
      "explanation": "AdaBoost often uses simple learners like decision stumps, combining many to form a strong model."
    },
    {
      "id": 24,
      "questionText": "Which is a limitation of Boosting?",
      "options": [
        "Cannot reduce bias",
        "Works only for regression",
        "Sensitive to noisy data and outliers",
        "Does not improve accuracy"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Boosting can overfit if data contains noise or outliers because later learners focus on these problematic points."
    },
    {
      "id": 25,
      "questionText": "Boosting is considered a strong learner because it:",
      "options": [
        "Is a single tree",
        "Combines multiple weak learners to reduce bias",
        "Reduces dataset size",
        "Ignores misclassified instances"
      ],
      "correctAnswerIndex": 1,
      "explanation": "By sequentially combining weak learners that correct each other’s errors, Boosting produces a high-accuracy strong model."
    },
    {
      "id": 26,
      "questionText": "XGBoost improves Gradient Boosting by:",
      "options": [
        "Adding regularization and efficient computation",
        "Reducing dataset size",
        "Using unsupervised trees",
        "Ignoring residuals"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost introduces L1/L2 regularization and optimized tree construction, improving generalization and speed."
    },
    {
      "id": 27,
      "questionText": "What happens if Boosting is applied with very complex base learners?",
      "options": [
        "Variance is ignored",
        "Overfitting is likely",
        "Bias reduces automatically",
        "Model becomes linear"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Using highly complex base learners in Boosting can lead to overfitting, especially if the number of learners is large."
    },
    {
      "id": 28,
      "questionText": "Why is learning rate important in Gradient Boosting?",
      "options": [
        "It selects features randomly",
        "It increases dataset size",
        "It prevents bootstrapping",
        "It controls the step size in gradient descent"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Learning rate scales the contribution of each tree in Gradient Boosting, affecting convergence and overfitting."
    },
    {
      "id": 29,
      "questionText": "Which of the following best describes Boosting?",
      "options": [
        "Clustering algorithm",
        "Sequential ensemble focusing on reducing bias",
        "Dimensionality reduction technique",
        "Parallel ensemble focusing on reducing variance"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Boosting sequentially trains models to correct errors, reducing bias and improving performance."
    },
    {
      "id": 30,
      "questionText": "Which approach can prevent overfitting in Boosting?",
      "options": [
        "Removing features randomly",
        "Early stopping and shrinkage (low learning rate)",
        "Increasing tree depth only",
        "Ignoring residuals"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Early stopping and low learning rate (shrinkage) prevent overfitting by controlling model complexity and contribution of each learner."
    },
    {
      "id": 31,
      "questionText": "In Gradient Boosting, what does a 'residual' represent?",
      "options": [
        "Tree depth",
        "Bootstrap sample size",
        "Sum of squared errors",
        "Difference between actual and predicted values"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Gradient Boosting fits each subsequent learner to the residuals (errors) of the previous model to improve predictions."
    },
    {
      "id": 32,
      "questionText": "Which parameter controls the complexity of trees in Gradient Boosting?",
      "options": [
        "Max depth of trees",
        "Learning rate",
        "Bootstrap fraction",
        "Number of samples"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Max depth limits tree complexity, preventing overfitting in Gradient Boosting models."
    },
    {
      "id": 33,
      "questionText": "In AdaBoost, increasing the number of weak learners too much may:",
      "options": [
        "Reduce training time",
        "Always improve performance",
        "Cause overfitting",
        "Reduce bias to zero"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Too many learners can overfit to training data, especially if noise exists."
    },
    {
      "id": 34,
      "questionText": "Gradient Boosting differs from AdaBoost in that it:",
      "options": [
        "Uses gradient descent to minimize a loss function",
        "Uses parallel training",
        "Does not adjust sample weights",
        "Only works for classification"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Gradient Boosting fits new models to the gradient of the loss function, optimizing model performance iteratively."
    },
    {
      "id": 35,
      "questionText": "Which technique helps prevent overfitting in Boosting?",
      "options": [
        "Increasing tree depth",
        "Shrinkage (lower learning rate)",
        "Ignoring residuals",
        "Using only one tree"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Reducing learning rate (shrinkage) controls contribution of each learner, preventing overfitting."
    },
    {
      "id": 36,
      "questionText": "Which scenario indicates Boosting might overfit?",
      "options": [
        "Balanced data with shallow trees",
        "Small dataset with low variance models",
        "Noisy training data with many iterations",
        "Parallel training of learners"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Boosting focuses on errors, so noisy data can lead the model to overfit to outliers with too many iterations."
    },
    {
      "id": 37,
      "questionText": "What is the purpose of 'early stopping' in Gradient Boosting?",
      "options": [
        "Reduce tree depth",
        "Stop adding trees when validation error stops improving",
        "Randomly drop trees",
        "Increase learning rate"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Early stopping halts model training once validation performance stops improving, avoiding overfitting."
    },
    {
      "id": 38,
      "questionText": "Which learning rate is preferable for Gradient Boosting with many trees?",
      "options": [
        "Low learning rate (0.01–0.1)",
        "Learning rate does not matter",
        "High learning rate (>0.5)",
        "Learning rate = 1 always"
      ],
      "correctAnswerIndex": 0,
      "explanation": "A low learning rate ensures stable learning and better generalization when many trees are used."
    },
    {
      "id": 39,
      "questionText": "In XGBoost, L1 and L2 regularization are used to:",
      "options": [
        "Increase tree depth automatically",
        "Reduce dataset size",
        "Prevent overfitting and improve generalization",
        "Increase learning rate"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Regularization penalizes complex models, reducing overfitting and improving generalization performance."
    },
    {
      "id": 40,
      "questionText": "Which type of base learners are typically used in Boosting?",
      "options": [
        "Shallow decision trees (stumps)",
        "Clustering models",
        "Deep neural networks",
        "Linear regression only"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Boosting usually uses simple base learners like shallow trees to incrementally improve performance."
    },
    {
      "id": 41,
      "questionText": "Which metric is commonly used to evaluate Boosting in regression tasks?",
      "options": [
        "F1-score",
        "AUC",
        "Mean Squared Error (MSE)",
        "Silhouette Score"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Regression evaluation typically uses metrics like MSE, RMSE, or MAE."
    },
    {
      "id": 42,
      "questionText": "In Gradient Boosting, the number of trees should be:",
      "options": [
        "Irrelevant",
        "Balanced with learning rate for optimal performance",
        "Always low",
        "As high as possible always"
      ],
      "correctAnswerIndex": 1,
      "explanation": "A low learning rate requires more trees; a high learning rate may need fewer trees. Balance is essential."
    },
    {
      "id": 43,
      "questionText": "Boosting is particularly effective for:",
      "options": [
        "High bias models",
        "High variance, low bias models",
        "Unsupervised learning",
        "Dimensionality reduction"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Boosting reduces bias by combining weak learners sequentially, improving predictions."
    },
    {
      "id": 44,
      "questionText": "Why does Boosting focus on misclassified instances?",
      "options": [
        "To improve overall model accuracy",
        "To reduce training time",
        "To ignore noisy data",
        "To increase bias"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Focusing on difficult samples ensures sequential learners correct mistakes, improving ensemble performance."
    },
    {
      "id": 45,
      "questionText": "Which of the following Boosting algorithms is gradient-based?",
      "options": [
        "Random Forest",
        "Bagging",
        "Gradient Boosting",
        "AdaBoost"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Gradient Boosting uses gradients of a loss function to guide sequential learning."
    },
    {
      "id": 46,
      "questionText": "Which parameter in Gradient Boosting controls the step size of updates?",
      "options": [
        "Max depth",
        "Number of features",
        "Learning rate",
        "Subsample fraction"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Learning rate scales each learner’s contribution, preventing overfitting and ensuring smooth convergence."
    },
    {
      "id": 47,
      "questionText": "Which technique helps reduce variance in Boosting?",
      "options": [
        "High learning rate",
        "Subsampling (stochastic gradient boosting)",
        "Increasing tree depth",
        "Using all features always"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Randomly subsampling data and features adds diversity among trees, reducing variance."
    },
    {
      "id": 48,
      "questionText": "Which approach is used in XGBoost to improve computational efficiency?",
      "options": [
        "Parallel tree construction",
        "Reducing dataset size arbitrarily",
        "Ignoring residuals",
        "Sequential single-thread building"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost optimizes training speed via parallel computation and efficient data structures."
    },
    {
      "id": 49,
      "questionText": "In AdaBoost, a weak learner with higher error receives:",
      "options": [
        "Ignored completely",
        "Lower weight in the final model",
        "Higher weight",
        "Same weight as others"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Learners with higher error contribute less to the final prediction; AdaBoost weights are proportional to accuracy."
    },
    {
      "id": 50,
      "questionText": "Which method helps prevent Boosting from overfitting on noisy datasets?",
      "options": [
        "Adding more learners",
        "Increasing tree depth",
        "Shrinkage (low learning rate) and early stopping",
        "Using only one tree"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Controlling contribution of each learner and halting training early reduces overfitting on noise."
    },
    {
      "id": 51,
      "questionText": "Gradient Boosting can optimize which type of loss functions?",
      "options": [
        "Only squared error",
        "Only absolute error",
        "Any differentiable loss function",
        "Only cross-entropy"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Gradient Boosting is flexible, capable of optimizing any differentiable loss suitable for the task."
    },
    {
      "id": 52,
      "questionText": "Which of the following is a practical use of Boosting?",
      "options": [
        "Dimensionality reduction",
        "Image clustering",
        "Fraud detection in banking",
        "Principal Component Analysis"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Boosting excels in classification tasks like fraud detection due to its high accuracy and bias reduction."
    },
    {
      "id": 53,
      "questionText": "Which combination prevents overfitting in Gradient Boosting?",
      "options": [
        "Single learner",
        "High learning rate and deep trees",
        "Many features only",
        "Low learning rate and limited tree depth"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Limiting tree complexity and using a lower learning rate ensures better generalization."
    },
    {
      "id": 54,
      "questionText": "Why is subsampling used in stochastic Gradient Boosting?",
      "options": [
        "To increase bias",
        "To increase training time",
        "To remove features",
        "To reduce correlation among trees and variance"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Randomly selecting subsets of data adds diversity, reducing variance while maintaining bias reduction."
    },
    {
      "id": 55,
      "questionText": "In Boosting, why might small weak learners perform better?",
      "options": [
        "They ignore residuals",
        "They remove features",
        "They increase bias drastically",
        "They reduce overfitting and allow incremental improvement"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Simple learners prevent overfitting and allow sequential models to improve predictions gradually."
    },
    {
      "id": 56,
      "questionText": "XGBoost uses which technique for missing values?",
      "options": [
        "Replace with zeros always",
        "Ignore missing values",
        "Learn default direction in trees automatically",
        "Drop rows with missing data"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost can handle missing values by learning the optimal default direction in the tree splits."
    },
    {
      "id": 57,
      "questionText": "Which Boosting variant is particularly fast and scalable?",
      "options": [
        "Random Forest",
        "Bagging",
        "XGBoost",
        "AdaBoost"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost uses optimized computation, parallelization, and regularization, making it fast and scalable for large datasets."
    },
    {
      "id": 58,
      "questionText": "Which technique in Boosting ensures sequential models learn from previous mistakes?",
      "options": [
        "Feature selection",
        "Clustering",
        "Parallel averaging",
        "Weighted samples or residual fitting"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Boosting adjusts weights (classification) or fits residuals (regression) to focus on errors from prior learners."
    },
    {
      "id": 59,
      "questionText": "Which factor most affects Boosting performance?",
      "options": [
        "Bootstrap fraction only",
        "Dataset size only",
        "Feature normalization only",
        "Learning rate, number of trees, and base learner complexity"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Performance depends on carefully balancing learning rate, number of learners, and the complexity of base learners."
    },
    {
      "id": 60,
      "questionText": "Why is Boosting sensitive to outliers?",
      "options": [
        "Because data is sampled randomly",
        "Because trees ignore residuals",
        "Because learning rate is always high",
        "Because subsequent learners focus on misclassified points"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Boosting emphasizes misclassified points, which can amplify the effect of outliers if not handled properly."
    },
    {
      "id": 61,
      "questionText": "Which of these parameters is tuned to avoid overfitting in XGBoost?",
      "options": [
        "Only learning rate",
        "Only bootstrap fraction",
        "Max depth, learning rate, number of estimators, and regularization",
        "Only max features"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Tuning these parameters ensures balanced bias-variance trade-off and prevents overfitting."
    },
    {
      "id": 62,
      "questionText": "Which advantage does Gradient Boosting have over AdaBoost?",
      "options": [
        "Works only with binary classification",
        "Flexible loss function optimization",
        "Uses decision stumps only",
        "Parallel computation only"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Gradient Boosting can optimize differentiable loss functions, allowing applications in regression and classification tasks."
    },
    {
      "id": 63,
      "questionText": "How does subsample fraction affect stochastic Gradient Boosting?",
      "options": [
        "Reduces correlation among trees and variance",
        "Reduces learning rate automatically",
        "Removes trees",
        "Increases bias only"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Randomly using a subset of data for each tree increases diversity and prevents overfitting."
    },
    {
      "id": 64,
      "questionText": "Why might small learning rate with many trees outperform high learning rate?",
      "options": [
        "More stable learning and reduced overfitting",
        "Removes noise automatically",
        "Reduces bias drastically",
        "Faster training"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Lower learning rate ensures gradual learning, allowing better generalization and avoiding overfitting."
    },
    {
      "id": 65,
      "questionText": "Which Boosting variant is commonly used for large-scale datasets?",
      "options": [
        "XGBoost",
        "Gradient Descent only",
        "AdaBoost",
        "Bagging"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost is optimized for speed and scalability, suitable for large datasets."
    },
    {
      "id": 66,
      "questionText": "Which scenario may cause Gradient Boosting to underperform?",
      "options": [
        "Shallow learners only",
        "Balanced data with low variance models",
        "High noise with extreme outliers",
        "Early stopping used"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Boosting focuses on misclassified points, so noisy datasets with outliers can mislead sequential learners."
    },
    {
      "id": 67,
      "questionText": "What is the effect of high tree depth in Gradient Boosting?",
      "options": [
        "Removes need for learning rate",
        "Reduces bias drastically",
        "May increase overfitting",
        "Always improves accuracy"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Deeper trees capture more details but may overfit the training data, especially in Boosting."
    },
    {
      "id": 68,
      "questionText": "What is the main purpose of regularization in XGBoost?",
      "options": [
        "Add more trees",
        "Remove residuals",
        "Increase learning rate automatically",
        "Reduce overfitting and improve generalization"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Regularization penalizes complex models to prevent overfitting and enhance generalization."
    },
    {
      "id": 69,
      "questionText": "Which parameter combination is key to tuning Boosting?",
      "options": [
        "Bootstrap fraction only",
        "Random seed only",
        "Number of features only",
        "Number of trees, learning rate, tree depth"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Balancing the number of trees, learning rate, and tree depth is crucial for optimal performance."
    },
    {
      "id": 70,
      "questionText": "Which approach increases Boosting model diversity and reduces correlation?",
      "options": [
        "Ignoring residuals",
        "Using single tree",
        "Stochastic subsampling of data or features",
        "Increasing tree depth"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Randomly subsampling rows or features creates diverse learners, improving ensemble robustness."
    },
    {
      "id": 71,
      "questionText": "A credit card company wants to detect fraud using Boosting. What should they be careful about?",
      "options": [
        "Outliers and class imbalance",
        "Number of features only",
        "Use unsupervised learning",
        "Shallow learners only"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Fraud datasets are highly imbalanced and contain outliers; Boosting can overfit if these are not handled properly."
    },
    {
      "id": 72,
      "questionText": "In a noisy regression dataset, using many deep trees in Gradient Boosting may:",
      "options": [
        "Reduce bias to zero automatically",
        "Always improve predictions",
        "Ignore residuals",
        "Overfit to noise and reduce generalization"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Deep trees capture noise as well as signal, which can lead to overfitting in Boosting."
    },
    {
      "id": 73,
      "questionText": "A machine learning engineer wants faster training on a large dataset with Gradient Boosting. What is a good approach?",
      "options": [
        "Increase tree depth drastically",
        "Use very high learning rate",
        "Use single tree only",
        "Use subsample fraction <1 and parallel processing (XGBoost)"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Stochastic subsampling and parallel computation improve speed while maintaining performance."
    },
    {
      "id": 74,
      "questionText": "Which scenario might make AdaBoost underperform?",
      "options": [
        "Balanced and clean data",
        "High noise in labels",
        "Low variance weak learners",
        "Small number of iterations"
      ],
      "correctAnswerIndex": 1,
      "explanation": "AdaBoost focuses on misclassified samples, so noisy labels can mislead the learning process."
    },
    {
      "id": 75,
      "questionText": "In Gradient Boosting, early stopping is used to:",
      "options": [
        "Always increase number of trees",
        "Increase learning rate automatically",
        "Reduce number of features",
        "Prevent overfitting by halting when validation error stops improving"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Early stopping monitors validation error and halts training when additional trees no longer improve performance."
    },
    {
      "id": 76,
      "questionText": "In XGBoost, why is column subsampling useful?",
      "options": [
        "Increases tree depth automatically",
        "Removes residuals",
        "Reduces correlation among trees and improves generalization",
        "Only affects training speed"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Randomly selecting a subset of features for each tree reduces correlation and overfitting."
    },
    {
      "id": 77,
      "questionText": "A dataset contains extreme outliers. Which Boosting strategy helps?",
      "options": [
        "Use robust loss function or limit tree depth",
        "Increase learning rate",
        "Use many deep trees",
        "Ignore the outliers"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Robust loss functions and shallow trees prevent Boosting from fitting outliers excessively."
    },
    {
      "id": 78,
      "questionText": "Gradient Boosting is being used for house price prediction. Which combination prevents overfitting?",
      "options": [
        "Shallow trees only with one iteration",
        "High learning rate and deep trees",
        "Ignore residuals",
        "Low learning rate and moderate tree depth"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Lower learning rate with appropriately sized trees ensures gradual learning and better generalization."
    },
    {
      "id": 79,
      "questionText": "Which is a key advantage of XGBoost over standard Gradient Boosting?",
      "options": [
        "Works only with small datasets",
        "Regularization and efficient computation",
        "No need for tuning",
        "Always reduces bias to zero"
      ],
      "correctAnswerIndex": 1,
      "explanation": "XGBoost adds L1/L2 regularization and computational optimizations, making it faster and less prone to overfitting."
    },
    {
      "id": 80,
      "questionText": "Which real-world scenario suits Boosting best?",
      "options": [
        "Linear regression with few samples",
        "Binary classification with imbalanced dataset",
        "Dimensionality reduction",
        "Unsupervised clustering"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Boosting is highly effective for classification problems, especially when the dataset is imbalanced or has complex patterns."
    },
    {
      "id": 81,
      "questionText": "Why might using very large trees in Boosting be harmful?",
      "options": [
        "Reduces computation time",
        "Always improves bias",
        "Removes residuals automatically",
        "Can overfit to noise and increase variance"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Complex trees capture noise, causing overfitting and reducing generalization."
    },
    {
      "id": 82,
      "questionText": "Which scenario requires tuning the learning rate and number of trees carefully?",
      "options": [
        "Random sampling",
        "Large Gradient Boosting models for structured data",
        "Unsupervised PCA",
        "Single decision stump for small data"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Learning rate and tree number must be balanced for stable learning and prevention of overfitting."
    },
    {
      "id": 83,
      "questionText": "A Gradient Boosting model shows high training accuracy but low validation accuracy. What could help?",
      "options": [
        "Increase learning rate",
        "Use fewer trees only",
        "Increase tree depth",
        "Reduce tree depth and use early stopping"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Controlling tree complexity and stopping training early mitigates overfitting."
    },
    {
      "id": 84,
      "questionText": "Which feature of Boosting allows it to handle difficult classification tasks?",
      "options": [
        "Ignoring residuals",
        "Parallel averaging of trees",
        "Sequential focus on misclassified instances",
        "Random feature selection only"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Boosting emphasizes errors from previous learners, improving accuracy on difficult cases."
    },
    {
      "id": 85,
      "questionText": "How can noisy labels in classification affect Boosting?",
      "options": [
        "Noise improves accuracy",
        "Residuals are unaffected",
        "Boosting ignores noisy labels automatically",
        "Learners may focus on noise, causing overfitting"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Boosting gives higher weights to misclassified points, which can include noisy labels, leading to overfitting."
    },
    {
      "id": 86,
      "questionText": "A Gradient Boosting model takes very long to train. Which strategy improves efficiency?",
      "options": [
        "Increase tree depth",
        "Reduce learning rate and use subsampling (stochastic GB)",
        "Add more features",
        "Use only one tree"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Subsampling data or features and using stochastic gradient boosting improves computational efficiency."
    },
    {
      "id": 87,
      "questionText": "In XGBoost, regularization parameters control:",
      "options": [
        "Learning rate only",
        "Model complexity and overfitting",
        "Subsampling only",
        "Tree depth only"
      ],
      "correctAnswerIndex": 1,
      "explanation": "L1/L2 regularization penalizes complex models, reducing overfitting."
    },
    {
      "id": 88,
      "questionText": "Which technique improves generalization in Boosting?",
      "options": [
        "Increasing learning rate",
        "Adding very deep trees only",
        "Ignoring residuals",
        "Stochastic sampling of data and features"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Randomly sampling rows and features introduces diversity and reduces correlation among learners."
    },
    {
      "id": 89,
      "questionText": "Which problem type is Boosting less suited for?",
      "options": [
        "Extremely noisy datasets",
        "Structured regression",
        "Fraud detection",
        "Binary classification"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Boosting may overfit on extremely noisy data because it focuses on correcting previous errors."
    },
    {
      "id": 90,
      "questionText": "Why is learning rate critical in Boosting?",
      "options": [
        "Reduces dataset size",
        "Controls incremental contribution of each learner",
        "Increases tree depth automatically",
        "Selects features randomly"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Learning rate scales each learner’s impact, affecting convergence and overfitting."
    },
    {
      "id": 91,
      "questionText": "Which parameter combination often requires tuning in real-world Boosting tasks?",
      "options": [
        "Bootstrap fraction only",
        "Number of features only",
        "Learning rate, number of trees, tree depth, subsample fraction",
        "Random seed only"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Balancing these parameters is crucial for model performance and generalization."
    },
    {
      "id": 92,
      "questionText": "Boosting can achieve better results than Bagging when:",
      "options": [
        "Variance is low",
        "Data is perfectly clean",
        "Only linear models are used",
        "Bias is high and sequential error correction is needed"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Boosting reduces bias by focusing on errors sequentially, while Bagging primarily reduces variance."
    },
    {
      "id": 93,
      "questionText": "A Gradient Boosting model predicts housing prices poorly on unseen data. Likely reason?",
      "options": [
        "Learning rate too low only",
        "Bias too low",
        "Training data too small only",
        "Overfitting due to deep trees or high learning rate"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Overfitting arises when trees are too deep or learning rate too high, harming generalization."
    },
    {
      "id": 94,
      "questionText": "Scenario: Boosting model for fraud detection shows high accuracy but low recall. What to improve?",
      "options": [
        "Use very high learning rate",
        "Increase tree depth only",
        "Adjust class weights or sampling to focus on minority class",
        "Reduce number of trees"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Class imbalance can cause Boosting to favor majority class; weighting or sampling helps improve recall."
    },
    {
      "id": 95,
      "questionText": "Which real-world application suits XGBoost the most?",
      "options": [
        "Predicting customer churn",
        "Image generation",
        "Clustering retail products",
        "PCA for dimensionality reduction"
      ],
      "correctAnswerIndex": 0,
      "explanation": "XGBoost excels at structured data problems like churn prediction due to high accuracy and handling of complex patterns."
    },
    {
      "id": 96,
      "questionText": "Scenario: Boosting is overfitting on noisy data. Recommended fix?",
      "options": [
        "Increase number of trees only",
        "Increase tree depth",
        "Reduce learning rate, shallow trees, early stopping",
        "Ignore residuals"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Controlling model complexity and learning rate helps reduce overfitting on noisy data."
    },
    {
      "id": 97,
      "questionText": "Which is a main strength of Boosting over Bagging?",
      "options": [
        "Always faster",
        "Reduces bias via sequential error correction",
        "No need to tune parameters",
        "Reduces variance only"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Boosting sequentially reduces bias by focusing on previous errors, while Bagging mainly reduces variance."
    },
    {
      "id": 98,
      "questionText": "Scenario: Using Boosting for medical diagnosis with class imbalance. Best strategy?",
      "options": [
        "Use class weighting or SMOTE with Boosting",
        "Use default parameters only",
        "Ignore minority class",
        "Reduce number of trees"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Balancing classes ensures minority class predictions are accurate in Boosting models."
    },
    {
      "id": 99,
      "questionText": "Why does XGBoost often outperform traditional Gradient Boosting?",
      "options": [
        "Only deeper trees",
        "Only more trees",
        "Regularization, parallelization, and optimized tree learning",
        "Only higher learning rate"
      ],
      "correctAnswerIndex": 2,
      "explanation": "XGBoost includes computational optimizations and regularization techniques, improving performance and generalization."
    },
    {
      "id": 100,
      "questionText": "Scenario: Boosting for credit risk classification performs poorly. Which strategy helps?",
      "options": [
        "High learning rate only",
        "Increase number of trees only",
        "Feature engineering, handling class imbalance, tuning learning rate and tree depth",
        "Ignore residuals"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Careful feature engineering, class balancing, and parameter tuning are critical for high-performing Boosting models."
    }
  ]
}