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{
"title": "Stacking Mastery: 100 MCQs",
"description": "A comprehensive set of 100 multiple-choice questions on Stacking ensemble learning, covering basic concepts, implementation, and theoretical understanding.",
"questions": [
{
"id": 1,
"questionText": "What is the main idea of Stacking in ensemble learning?",
"options": [
"Train models in parallel and average results",
"Train sequential models to reduce bias",
"Use only one strong learner",
"Combine predictions of multiple models using a meta-learner"
],
"correctAnswerIndex": 3,
"explanation": "Stacking involves combining different base learners' predictions with a meta-learner to improve overall performance."
},
{
"id": 2,
"questionText": "Which component in Stacking combines the outputs of base learners?",
"options": [
"Residual estimator",
"Bootstrap sample",
"Decision stump",
"Meta-learner"
],
"correctAnswerIndex": 3,
"explanation": "The meta-learner takes predictions of base learners as input and produces the final output."
},
{
"id": 3,
"questionText": "Stacking differs from Bagging because it:",
"options": [
"Uses a meta-learner to combine predictions",
"Only reduces variance",
"Trains models independently",
"Uses bootstrapped samples only"
],
"correctAnswerIndex": 0,
"explanation": "Stacking focuses on learning the best combination of base learners via a meta-model."
},
{
"id": 4,
"questionText": "Which of the following is a typical base learner in Stacking?",
"options": [
"Meta-learner",
"Feature selector",
"Residual predictor",
"Decision tree"
],
"correctAnswerIndex": 3,
"explanation": "Decision trees, logistic regression, or other models can serve as base learners."
},
{
"id": 5,
"questionText": "Which of these is a common meta-learner?",
"options": [
"Decision stump",
"Bootstrap sample",
"Logistic regression",
"PCA"
],
"correctAnswerIndex": 2,
"explanation": "Logistic regression or linear regression is often used as a simple meta-learner to combine predictions."
},
{
"id": 6,
"questionText": "Stacking is most useful when base learners are:",
"options": [
"Highly correlated",
"Identical models",
"Extremely simple only",
"Diverse in type or error patterns"
],
"correctAnswerIndex": 3,
"explanation": "Diversity among base learners allows the meta-learner to exploit complementary strengths."
},
{
"id": 7,
"questionText": "What is the main advantage of Stacking?",
"options": [
"Reduces training time",
"Improves predictive performance by combining multiple models",
"Always reduces bias to zero",
"Eliminates the need for parameter tuning"
],
"correctAnswerIndex": 1,
"explanation": "By learning from multiple base models, Stacking often achieves higher accuracy than any single model."
},
{
"id": 8,
"questionText": "In Stacking, which data is used to train the meta-learner?",
"options": [
"Original training data only",
"Residuals of base learners",
"Randomly generated features",
"Predictions of base learners on validation or out-of-fold data"
],
"correctAnswerIndex": 3,
"explanation": "Using out-of-fold predictions prevents overfitting when training the meta-learner."
},
{
"id": 9,
"questionText": "Which is a difference between Stacking and Boosting?",
"options": [
"Stacking reduces variance only",
"Boosting uses meta-learners, Stacking does not",
"Stacking combines models in parallel, Boosting sequentially",
"Boosting uses multiple meta-learners"
],
"correctAnswerIndex": 2,
"explanation": "Boosting trains models sequentially to correct errors, while Stacking trains models independently and combines their predictions."
},
{
"id": 10,
"questionText": "Why is cross-validation often used in Stacking?",
"options": [
"To select meta-learner automatically",
"To increase learning rate",
"To train base learners faster",
"To generate out-of-fold predictions for training the meta-learner"
],
"correctAnswerIndex": 3,
"explanation": "Cross-validation provides unbiased predictions of base learners on data not seen during training, which is used to train the meta-learner."
},
{
"id": 11,
"questionText": "Stacking is also known as:",
"options": [
"Random forest ensemble",
"Boosted regression",
"Stacked generalization",
"Sequential bagging"
],
"correctAnswerIndex": 2,
"explanation": "Stacking was introduced as 'stacked generalization' by Wolpert to combine multiple models."
},
{
"id": 12,
"questionText": "Which problem does Stacking address that single models might struggle with?",
"options": [
"Reducing dataset size",
"Combining strengths of different algorithms for better generalization",
"Faster training",
"Feature scaling"
],
"correctAnswerIndex": 1,
"explanation": "Stacking leverages different models to capture various patterns and reduce generalization error."
},
{
"id": 13,
"questionText": "In a classification task, what type of output is passed to the meta-learner?",
"options": [
"Random noise",
"Residuals only",
"Predicted probabilities or labels from base learners",
"Original features only"
],
"correctAnswerIndex": 2,
"explanation": "The meta-learner uses predictions (labels or probabilities) from base learners to make final predictions."
},
{
"id": 14,
"questionText": "Which is true about the diversity of base learners in Stacking?",
"options": [
"All base learners should be identical",
"Greater diversity usually improves ensemble performance",
"Meta-learner must be a tree",
"Only deep trees are used"
],
"correctAnswerIndex": 1,
"explanation": "Different algorithms or parameter settings increase diversity and help the ensemble learn better."
},
{
"id": 15,
"questionText": "Which dataset is used to prevent overfitting of the meta-learner?",
"options": [
"Random subset of test data",
"Entire training set predictions",
"Out-of-fold predictions from training set",
"Residual errors only"
],
"correctAnswerIndex": 2,
"explanation": "Out-of-fold predictions give unbiased estimates for the meta-learner to learn safely."
},
{
"id": 16,
"questionText": "Stacking can be applied to:",
"options": [
"Unsupervised tasks only",
"Both classification and regression tasks",
"Only classification",
"Only regression"
],
"correctAnswerIndex": 1,
"explanation": "Stacking is versatile and can combine base learners for both regression and classification tasks."
},
{
"id": 17,
"questionText": "Which is NOT a typical base learner in Stacking?",
"options": [
"Logistic regression",
"Decision tree",
"KNN",
"Random noise generator"
],
"correctAnswerIndex": 3,
"explanation": "Random noise is not a meaningful base learner and cannot contribute to ensemble learning."
},
{
"id": 18,
"questionText": "Meta-learner complexity should be:",
"options": [
"Always very deep",
"Same as base learner complexity",
"Simple enough to avoid overfitting on base predictions",
"Randomly selected"
],
"correctAnswerIndex": 2,
"explanation": "A simple meta-learner generalizes better by learning patterns from base predictions without overfitting."
},
{
"id": 19,
"questionText": "Which of the following can be used as meta-learner?",
"options": [
"Random features only",
"Bootstrap samples",
"Noise vector",
"Linear regression, logistic regression, or tree"
],
"correctAnswerIndex": 3,
"explanation": "Various models can serve as meta-learner depending on the problem type."
},
{
"id": 20,
"questionText": "Stacking usually improves performance when base learners:",
"options": [
"Have complementary strengths and weaknesses",
"Are identical in type",
"Have zero diversity",
"Are only weak learners"
],
"correctAnswerIndex": 0,
"explanation": "Combining models with different strengths allows the meta-learner to correct errors and improve predictions."
},
{
"id": 21,
"questionText": "Which is a common mistake when implementing Stacking?",
"options": [
"Using simple meta-learner",
"Using cross-validation for base predictions",
"Training meta-learner on same data base learners saw",
"Using different base learners"
],
"correctAnswerIndex": 2,
"explanation": "Training meta-learner on same data can cause overfitting; out-of-fold predictions prevent this."
},
{
"id": 22,
"questionText": "Stacking differs from Voting because:",
"options": [
"It reduces variance only",
"It averages predictions blindly",
"It learns weights using a meta-learner rather than using fixed rules",
"It uses bootstrap samples only"
],
"correctAnswerIndex": 2,
"explanation": "Unlike Voting, Stacking trains a model to optimally combine base learners’ predictions."
},
{
"id": 23,
"questionText": "Which scenario benefits most from Stacking?",
"options": [
"Identical models only",
"Single model with high accuracy",
"When multiple different models have complementary predictive power",
"Very small datasets"
],
"correctAnswerIndex": 2,
"explanation": "Stacking leverages diverse models to produce better generalization than any individual model."
},
{
"id": 24,
"questionText": "Which metric should you use to evaluate Stacking?",
"options": [
"Depends on the problem (accuracy, RMSE, F1, etc.)",
"Always F1-score",
"Always RMSE",
"Always accuracy"
],
"correctAnswerIndex": 0,
"explanation": "Evaluation metric depends on the type of task (classification or regression)."
},
{
"id": 25,
"questionText": "In K-fold Stacking, each fold provides predictions to:",
"options": [
"Train the meta-learner without overfitting",
"Generate residuals",
"Train base learners only",
"Randomly select features"
],
"correctAnswerIndex": 0,
"explanation": "K-fold cross-validation provides unbiased predictions from base learners for the meta-learner."
},
{
"id": 26,
"questionText": "Stacking can reduce generalization error by:",
"options": [
"Randomly averaging predictions",
"Ignoring base learners",
"Combining strengths of multiple models",
"Using only a single strong model"
],
"correctAnswerIndex": 2,
"explanation": "Meta-learner exploits complementary strengths of base learners to improve predictions."
},
{
"id": 27,
"questionText": "Which is true for regression tasks using Stacking?",
"options": [
"Meta-learner predicts labels only",
"Only classification is possible",
"Residuals are ignored",
"Base learners predict continuous values, meta-learner combines them"
],
"correctAnswerIndex": 3,
"explanation": "For regression, the meta-learner learns to combine continuous predictions from base learners."
},
{
"id": 28,
"questionText": "Which prevents overfitting in Stacking?",
"options": [
"Ignoring diversity of base learners",
"Deep meta-learner only",
"Using out-of-fold predictions for meta-learner training",
"Training meta-learner on entire dataset predictions"
],
"correctAnswerIndex": 2,
"explanation": "Out-of-fold predictions prevent the meta-learner from memorizing base learners’ predictions."
},
{
"id": 29,
"questionText": "Scenario: Combining Random Forest, SVM, and KNN with a linear meta-learner. This is:",
"options": [
"Boosting",
"Bagging",
"Stacking",
"Voting"
],
"correctAnswerIndex": 2,
"explanation": "Different base learners are combined via a meta-learner, which defines Stacking."
},
{
"id": 30,
"questionText": "Which is the main requirement for base learners in Stacking?",
"options": [
"They must be deep trees only",
"They should be diverse and not perfectly correlated",
"They should always be linear models",
"They must have identical predictions"
],
"correctAnswerIndex": 1,
"explanation": "Diversity ensures that the meta-learner can learn from complementary strengths of different models."
},
{
"id": 31,
"questionText": "In Stacking, why is it important that base learners are diverse?",
"options": [
"Identical base learners are always better",
"Diversity increases bias",
"Diverse base learners capture different aspects of the data, improving meta-learner performance",
"Diversity reduces computation"
],
"correctAnswerIndex": 2,
"explanation": "Diversity among base learners ensures complementary strengths, which the meta-learner can exploit for better predictions."
},
{
"id": 32,
"questionText": "Which technique is commonly used to generate unbiased predictions for meta-learner training?",
"options": [
"K-fold cross-validation (out-of-fold predictions)",
"Random feature selection",
"Using test data",
"Bootstrap sampling only"
],
"correctAnswerIndex": 0,
"explanation": "K-fold cross-validation produces predictions from unseen data folds to prevent overfitting when training the meta-learner."
},
{
"id": 33,
"questionText": "Scenario: You use three base learners with high correlation. What is likely to happen?",
"options": [
"The meta-learner ignores correlation automatically",
"Performance will drastically improve",
"Overfitting is impossible",
"The meta-learner gains little benefit due to redundant information"
],
"correctAnswerIndex": 3,
"explanation": "Highly correlated base learners do not provide complementary information, reducing the benefit of Stacking."
},
{
"id": 34,
"questionText": "Which type of meta-learner is commonly used for regression tasks?",
"options": [
"Decision stump",
"Logistic regression",
"Linear regression or ridge regression",
"Random noise generator"
],
"correctAnswerIndex": 2,
"explanation": "Linear or regularized regression models are simple and effective for combining continuous outputs of base learners."
},
{
"id": 35,
"questionText": "Which type of meta-learner is commonly used for classification tasks?",
"options": [
"K-means clustering",
"Random noise generator",
"Logistic regression",
"Linear regression"
],
"correctAnswerIndex": 2,
"explanation": "Logistic regression can combine probability outputs from base learners and produce final class probabilities."
},
{
"id": 36,
"questionText": "Stacking can be applied to:",
"options": [
"Classification and regression",
"Unsupervised tasks only",
"Only classification",
"Only regression"
],
"correctAnswerIndex": 0,
"explanation": "Stacking is versatile and works for both classification and regression problems."
},
{
"id": 37,
"questionText": "Scenario: Base learners perform poorly individually but differently. Stacking may:",
"options": [
"Always fail",
"Reduce bias only",
"Increase correlation among predictions",
"Improve overall performance by combining diverse predictions"
],
"correctAnswerIndex": 3,
"explanation": "Even weak base learners can be combined effectively by the meta-learner if they make different errors."
},
{
"id": 38,
"questionText": "Why should meta-learner complexity be limited?",
"options": [
"To prevent overfitting on base learners’ predictions",
"To reduce dataset size",
"To increase training time",
"Because base learners are always simple"
],
"correctAnswerIndex": 0,
"explanation": "A simple meta-learner generalizes better on predictions from base learners without memorizing noise."
},
{
"id": 39,
"questionText": "Scenario: Using Random Forest, SVM, and KNN as base learners with Logistic Regression as meta-learner. Which is true?",
"options": [
"Diverse base learners + simple meta-learner is a common Stacking setup",
"Base learners must be identical",
"Meta-learner should be very deep",
"Only regression problems are supported"
],
"correctAnswerIndex": 0,
"explanation": "Combining different algorithms with a simple meta-learner is a standard approach in Stacking."
},
{
"id": 40,
"questionText": "Scenario: Your meta-learner overfits the base learners’ predictions. Which solution is suitable?",
"options": [
"Use simpler meta-learner or regularization",
"Add more base learners without change",
"Increase base learner complexity",
"Ignore cross-validation"
],
"correctAnswerIndex": 0,
"explanation": "Regularizing or simplifying the meta-learner reduces overfitting on base predictions."
},
{
"id": 41,
"questionText": "Which cross-validation strategy is used to generate predictions for meta-learner training?",
"options": [
"Random sampling",
"No CV is needed",
"K-fold cross-validation",
"Leave-one-out only"
],
"correctAnswerIndex": 2,
"explanation": "K-fold CV produces out-of-fold predictions to prevent overfitting of the meta-learner."
},
{
"id": 42,
"questionText": "Stacking differs from Voting because:",
"options": [
"It learns combination weights via a meta-learner",
"It reduces variance only",
"It uses identical base learners",
"It averages predictions blindly"
],
"correctAnswerIndex": 0,
"explanation": "Voting combines base learners using fixed rules, while Stacking learns how to combine predictions optimally."
},
{
"id": 43,
"questionText": "Scenario: Your dataset is small. Stacking may:",
"options": [
"Always improve accuracy",
"Overfit due to limited training data for meta-learner",
"Reduce computation time automatically",
"Ignore base learners"
],
"correctAnswerIndex": 1,
"explanation": "Meta-learner may overfit if there isn’t enough data for unbiased predictions from base learners."
},
{
"id": 44,
"questionText": "Which situation is ideal for using Stacking?",
"options": [
"Highly correlated base learners",
"No training data available",
"Single strong model is sufficient",
"Multiple different models have complementary strengths"
],
"correctAnswerIndex": 3,
"explanation": "Stacking benefits when base learners make different types of errors, allowing meta-learner to combine them effectively."
},
{
"id": 45,
"questionText": "Why are out-of-fold predictions used instead of training predictions for the meta-learner?",
"options": [
"To add noise intentionally",
"To prevent meta-learner from overfitting",
"To reduce computation",
"To increase correlation"
],
"correctAnswerIndex": 1,
"explanation": "Using predictions on unseen folds ensures the meta-learner sees unbiased predictions and generalizes better."
},
{
"id": 46,
"questionText": "Scenario: All base learners are trees with same depth. How to improve stacking?",
"options": [
"Use only meta-learner",
"Add more identical trees",
"Reduce training data",
"Increase diversity via different algorithms or hyperparameters"
],
"correctAnswerIndex": 3,
"explanation": "Diverse learners are key for stacking; otherwise, meta-learner gains little new information."
},
{
"id": 47,
"questionText": "Which of the following helps prevent overfitting in stacking?",
"options": [
"Adding noise to predictions",
"Deep meta-learner only",
"High learning rate only",
"Cross-validation, simpler meta-learner, regularization"
],
"correctAnswerIndex": 3,
"explanation": "Using CV and regularization ensures meta-learner does not memorize base learners’ predictions."
},
{
"id": 48,
"questionText": "Which task is stacking suitable for?",
"options": [
"Structured regression, classification, and hybrid tasks",
"Only unsupervised learning",
"Only image generation",
"Only dimensionality reduction"
],
"correctAnswerIndex": 0,
"explanation": "Stacking is versatile and can be applied to any supervised task."
},
{
"id": 49,
"questionText": "Scenario: You want to combine a Random Forest and a KNN for classification. What is a suitable meta-learner?",
"options": [
"Logistic regression",
"K-means clustering",
"Principal Component Analysis",
"Another Random Forest only"
],
"correctAnswerIndex": 0,
"explanation": "A simple model like logistic regression can effectively combine predictions from heterogeneous base learners."
},
{
"id": 50,
"questionText": "Why is meta-learner training data usually smaller than base learner training data?",
"options": [
"It sees random features only",
"It uses the entire dataset again",
"It only sees residuals",
"It uses out-of-fold predictions from base learners"
],
"correctAnswerIndex": 3,
"explanation": "Meta-learner sees predictions on validation folds, not full training data, to avoid overfitting."
},
{
"id": 51,
"questionText": "Scenario: Base learners predict different class probabilities for a sample. What does the meta-learner do?",
"options": [
"Selects the first base learner only",
"Combines these predictions to make the final decision",
"Averages features instead of predictions",
"Ignores all predictions"
],
"correctAnswerIndex": 1,
"explanation": "The meta-learner uses outputs from base learners as inputs to produce a more accurate final prediction."
},
{
"id": 52,
"questionText": "Which of these is a benefit of using Stacking over individual models?",
"options": [
"Reduces dataset size automatically",
"Improved predictive performance by combining strengths of multiple models",
"Always faster training",
"No need for cross-validation"
],
"correctAnswerIndex": 1,
"explanation": "Stacking leverages diverse models to capture different patterns and reduce overall error."
},
{
"id": 53,
"questionText": "Scenario: Stacking with highly correlated base learners results in:",
"options": [
"Limited improvement due to redundant predictions",
"No need for a meta-learner",
"Automatic error correction",
"Maximum improvement always"
],
"correctAnswerIndex": 0,
"explanation": "If base learners make similar errors, the meta-learner gains little new information."
},
{
"id": 54,
"questionText": "Which factor is crucial for effective Stacking?",
"options": [
"Training base learners on same features only",
"Identical predictions from all base learners",
"Diversity among base learners",
"Using a deep meta-learner only"
],
"correctAnswerIndex": 2,
"explanation": "Different algorithms or parameters ensure base learners capture complementary information."
},
{
"id": 55,
"questionText": "Scenario: Small dataset, multiple base learners. Meta-learner shows overfitting. Recommended solution?",
"options": [
"Increase number of trees only",
"Ignore cross-validation",
"Increase meta-learner complexity",
"Use simpler meta-learner or regularization, possibly reduce number of base learners"
],
"correctAnswerIndex": 3,
"explanation": "Simpler meta-learner and regularization prevent overfitting when training data is limited."
},
{
"id": 56,
"questionText": "Why is stacking preferred over simple averaging or voting in some cases?",
"options": [
"It always uses deep learning",
"It learns optimal weights for combining predictions instead of using fixed rules",
"It eliminates need for base learners",
"It reduces computation time"
],
"correctAnswerIndex": 1,
"explanation": "The meta-learner can adaptively combine base predictions based on data patterns, improving accuracy."
},
{
"id": 57,
"questionText": "Scenario: Base learners are decision trees with shallow depth. Meta-learner is logistic regression. Likely effect?",
"options": [
"Meta-learner can capture complementary signals and improve performance",
"Performance will always drop",
"Trees become irrelevant",
"Only overfitting occurs"
],
"correctAnswerIndex": 0,
"explanation": "Even weak or shallow learners can provide useful signals for the meta-learner."
},
{
"id": 58,
"questionText": "Which is a common mistake in Stacking implementation?",
"options": [
"Using simple meta-learner",
"Training meta-learner on base learners’ training predictions (not out-of-fold predictions)",
"Using diverse base learners",
"Cross-validation for base predictions"
],
"correctAnswerIndex": 1,
"explanation": "Using training predictions directly can cause overfitting; out-of-fold predictions are needed."
},
{
"id": 59,
"questionText": "Scenario: Stacking regression with three base learners. Which output type does the meta-learner use?",
"options": [
"Predicted classes only",
"Random noise vector",
"Residuals only",
"Predicted continuous values from base learners"
],
"correctAnswerIndex": 3,
"explanation": "Meta-learner combines predicted continuous outputs from base learners to produce final regression output."
},
{
"id": 60,
"questionText": "Scenario: You have Random Forest, XGBoost, and SVM as base learners. Which meta-learner is simple and effective?",
"options": [
"PCA",
"Deep neural network only",
"Logistic regression or linear regression",
"Random noise generator"
],
"correctAnswerIndex": 2,
"explanation": "Simple regression models can effectively combine heterogeneous predictions without overfitting."
},
{
"id": 61,
"questionText": "Scenario: Meta-learner predicts perfectly on training data but poorly on test data. Cause?",
"options": [
"Dataset too large",
"Meta-learner too simple",
"Overfitting due to using training predictions instead of out-of-fold predictions",
"Base learners are too diverse"
],
"correctAnswerIndex": 2,
"explanation": "Training on base learners’ predictions from the same data leads to memorization and poor generalization."
},
{
"id": 62,
"questionText": "Which of these is NOT a recommended strategy in Stacking?",
"options": [
"Using out-of-fold predictions",
"Using cross-validation for base learners",
"Regularizing the meta-learner",
"Using meta-learner trained on base learners’ training predictions"
],
"correctAnswerIndex": 3,
"explanation": "Meta-learner must be trained on unbiased predictions; using training predictions causes overfitting."
},
{
"id": 63,
"questionText": "Scenario: Base learners have high variance individually. Stacking can:",
"options": [
"Always increase bias",
"Reduce overall variance by combining their predictions",
"Ignore base learner predictions",
"Eliminate need for cross-validation"
],
"correctAnswerIndex": 1,
"explanation": "Meta-learner can combine different noisy predictions to reduce overall variance and improve stability."
},
{
"id": 64,
"questionText": "Scenario: Base learners are homogeneous (e.g., all logistic regressions). Likely effect?",
"options": [
"Meta-learner ignored",
"Maximum benefit always",
"Overfitting impossible",
"Limited improvement from Stacking due to redundancy"
],
"correctAnswerIndex": 3,
"explanation": "Stacking works best when base learners are diverse; homogeneous models provide little new information."
},
{
"id": 65,
"questionText": "Which approach improves stacking with limited data?",
"options": [
"More complex meta-learner only",
"Ignore base learner diversity",
"Regularization, simpler meta-learner, careful cross-validation",
"Train meta-learner on training predictions"
],
"correctAnswerIndex": 2,
"explanation": "These strategies reduce overfitting and improve generalization when data is scarce."
},
{
"id": 66,
"questionText": "Scenario: Meta-learner underfits base predictions. Recommended fix?",
"options": [
"Use training predictions instead of out-of-fold",
"Reduce base learner diversity",
"Use a slightly more complex meta-learner or additional features",
"Ignore predictions"
],
"correctAnswerIndex": 2,
"explanation": "A slightly more flexible meta-learner can better capture relationships between base learners’ predictions."
},
{
"id": 67,
"questionText": "Scenario: Combining Random Forest and Gradient Boosting as base learners. Which advantage does stacking provide?",
"options": [
"Eliminates bias automatically",
"Leverages complementary strengths of ensemble methods for better prediction",
"Reduces variance to zero",
"Replaces base learners completely"
],
"correctAnswerIndex": 1,
"explanation": "Stacking allows different ensembles to complement each other, improving overall performance."
},
{
"id": 68,
"questionText": "Scenario: Using stacking in classification, base learners predict probabilities. Meta-learner input?",
"options": [
"Random noise vector",
"Predicted probabilities from base learners",
"Original features only",
"Residual errors only"
],
"correctAnswerIndex": 1,
"explanation": "Meta-learner uses predicted probabilities from base learners as inputs to produce final classification."
},
{
"id": 69,
"questionText": "Which scenario would reduce the benefit of stacking?",
"options": [
"Base learners are diverse",
"Base learners are highly correlated",
"Out-of-fold predictions are used",
"Meta-learner is regularized"
],
"correctAnswerIndex": 1,
"explanation": "High correlation among base learners provides redundant information, limiting stacking’s advantage."
},
{
"id": 70,
"questionText": "Scenario: Stacking regression task shows overfitting. First check:",
"options": [
"Whether meta-learner was trained on out-of-fold predictions",
"Base learner type only",
"Number of features only",
"Dataset size only"
],
"correctAnswerIndex": 0,
"explanation": "Using training predictions instead of out-of-fold predictions is a common cause of overfitting in stacking."
},
{
"id": 71,
"questionText": "Scenario: In a Kaggle competition, you combine multiple tree-based and linear models. Your meta-learner performs worse than individual base learners. Likely cause?",
"options": [
"Base learners are too diverse",
"Dataset is too large",
"Meta-learner overfitted due to training on base learners’ training predictions",
"Meta-learner is too simple"
],
"correctAnswerIndex": 2,
"explanation": "Training the meta-learner on the same data as base learners can cause memorization and poor generalization."
},
{
"id": 72,
"questionText": "Scenario: You notice highly correlated predictions from base learners. Which action is appropriate?",
"options": [
"Ignore the correlation",
"Increase number of trees in all learners",
"Introduce more diverse base learners",
"Use the same algorithm with different hyperparameters only"
],
"correctAnswerIndex": 2,
"explanation": "High correlation reduces the benefit of stacking. Introducing diverse models captures complementary patterns."
},
{
"id": 73,
"questionText": "Scenario: Base learners are neural networks with slightly different architectures. Meta-learner is linear regression. What is expected?",
"options": [
"Meta-learner can combine complementary predictions to improve accuracy",
"Performance always decreases",
"Meta-learner will ignore base learners",
"Stacking will fail because linear models cannot handle neural networks"
],
"correctAnswerIndex": 0,
"explanation": "Linear meta-learner can learn optimal weights for combining diverse neural network outputs."
},
{
"id": 74,
"questionText": "Scenario: Using stacking for regression, meta-learner outputs extreme values. Cause?",
"options": [
"Base learners’ predictions are poorly scaled or meta-learner is too complex",
"Base learners are too diverse",
"Meta-learner underfitted",
"Dataset is too small"
],
"correctAnswerIndex": 0,
"explanation": "Improper scaling or an overly complex meta-learner can lead to extreme predictions."
},
{
"id": 75,
"questionText": "Scenario: You stack three models and notice high variance in meta-learner. Solution?",
"options": [
"Add more identical base learners",
"Regularize meta-learner or reduce complexity",
"Ignore variance",
"Use training predictions instead of out-of-fold"
],
"correctAnswerIndex": 1,
"explanation": "Regularization prevents meta-learner from overfitting to noisy base learner predictions."
},
{
"id": 76,
"questionText": "Scenario: Base learners perform poorly individually but differently. Stacking improves results. Why?",
"options": [
"Base learners are ignored",
"Meta-learner leverages complementary errors to produce better overall predictions",
"Stacking magically improves all models",
"Random averaging occurs"
],
"correctAnswerIndex": 1,
"explanation": "Even weak but diverse models can be combined effectively by the meta-learner."
},
{
"id": 77,
"questionText": "Scenario: Meta-learner is too powerful (e.g., deep neural network). What is the likely outcome?",
"options": [
"Improved generalization automatically",
"Overfitting to base learners’ predictions",
"Dataset size decreases",
"Base learners become irrelevant"
],
"correctAnswerIndex": 1,
"explanation": "Overly complex meta-learner may memorize base predictions instead of learning patterns, leading to poor generalization."
},
{
"id": 78,
"questionText": "Scenario: Small dataset with many base learners. Meta-learner underfits. Solution?",
"options": [
"Reduce base learner complexity or number",
"Train on test data",
"Ignore diversity",
"Increase meta-learner complexity"
],
"correctAnswerIndex": 0,
"explanation": "Too many base learners can overwhelm meta-learner on small datasets. Reducing base learners or their complexity helps."
},
{
"id": 79,
"questionText": "Scenario: Regression stacking task shows systematic bias. Solution?",
"options": [
"Adjust meta-learner to correct bias or apply residual correction",
"Use training predictions instead of out-of-fold",
"Increase number of base learners only",
"Ignore base learners"
],
"correctAnswerIndex": 0,
"explanation": "Meta-learner can be tuned or trained on residuals to correct systematic bias."
},
{
"id": 80,
"questionText": "Scenario: Ensemble includes Random Forest, XGBoost, and KNN. Test accuracy decreases after stacking. First check?",
"options": [
"Whether meta-learner was trained on proper out-of-fold predictions",
"Number of trees only",
"Feature selection only",
"Dataset size only"
],
"correctAnswerIndex": 0,
"explanation": "Improper meta-learner training is the most common cause of poor stacking performance."
},
{
"id": 81,
"questionText": "Scenario: You want to combine multiple image classifiers via stacking. Which approach is suitable?",
"options": [
"Use softmax probabilities from base classifiers as meta-learner input",
"Use raw pixel inputs",
"Ignore base classifiers",
"Average features randomly"
],
"correctAnswerIndex": 0,
"explanation": "Meta-learner combines probability predictions rather than raw data for effective stacking."
},
{
"id": 82,
"questionText": "Scenario: In a stacking setup, meta-learner shows perfect training accuracy. Likely issue?",
"options": [
"Overfitting due to using base learners’ training predictions",
"Base learners are too diverse",
"Meta-learner too simple",
"Dataset too small"
],
"correctAnswerIndex": 0,
"explanation": "Perfect training accuracy is a sign of overfitting; out-of-fold predictions prevent this."
},
{
"id": 83,
"questionText": "Scenario: Base learners are all SVMs with different kernels. Meta-learner is logistic regression. Likely outcome?",
"options": [
"Improved generalization due to diversity in kernel functions",
"No improvement, identical predictions",
"Overfitting impossible",
"Meta-learner ignored"
],
"correctAnswerIndex": 0,
"explanation": "Different kernels capture complementary patterns, allowing meta-learner to improve predictions."
},
{
"id": 84,
"questionText": "Scenario: Base learners have high variance errors. Stacking improves predictions. Why?",
"options": [
"Meta-learner combines predictions to reduce variance and improve stability",
"Stacking magically reduces errors",
"Base learners are ignored",
"Random averaging occurs"
],
"correctAnswerIndex": 0,
"explanation": "Meta-learner can smooth out high variance by learning the optimal combination of predictions."
},
{
"id": 85,
"questionText": "Scenario: Regression stacking task shows systematic bias. Solution?",
"options": [
"Adjust meta-learner to correct bias or apply residual correction",
"Ignore base learners",
"Increase number of base learners only",
"Use training predictions instead of out-of-fold"
],
"correctAnswerIndex": 0,
"explanation": "Meta-learner can be tuned or trained on residuals to correct systematic bias."
},
{
"id": 86,
"questionText": "Scenario: Base learners predict probabilities for multi-class classification. Meta-learner input?",
"options": [
"Concatenated class probabilities from all base learners",
"Raw features only",
"Residuals only",
"Random noise vector"
],
"correctAnswerIndex": 0,
"explanation": "Meta-learner uses predicted probabilities from all classes to make the final decision."
},
{
"id": 87,
"questionText": "Scenario: Meta-learner underfits in a classification stacking task. Recommended action?",
"options": [
"Increase meta-learner capacity slightly or add engineered features",
"Reduce base learner diversity",
"Ignore base learners",
"Train meta-learner on training predictions"
],
"correctAnswerIndex": 0,
"explanation": "A slightly more complex meta-learner can capture relationships between base learners’ outputs."
},
{
"id": 88,
"questionText": "Scenario: Small dataset, multiple base learners. Meta-learner overfits. Best solution?",
"options": [
"Use simpler meta-learner and regularization",
"Add more base learners",
"Ignore cross-validation",
"Train meta-learner on training predictions"
],
"correctAnswerIndex": 0,
"explanation": "Simpler meta-learner with regularization prevents overfitting on limited out-of-fold predictions."
},
{
"id": 89,
"questionText": "Scenario: Base learners include gradient boosting, random forest, and logistic regression. Stacking improves performance. Why?",
"options": [
"Meta-learner exploits complementary predictions of heterogeneous models",
"Stacking magically improves results",
"Base learners are ignored",
"Dataset size increases"
],
"correctAnswerIndex": 0,
"explanation": "Diverse models capture different patterns, which meta-learner combines for better generalization."
},
{
"id": 90,
"questionText": "Scenario: You want to stack deep learning models for regression. Best approach?",
"options": [
"Use predicted outputs or features from penultimate layers as meta-learner input",
"Raw images only",
"Ignore base learners",
"Average base model weights"
],
"correctAnswerIndex": 0,
"explanation": "Using predictions or embeddings from deep models is standard for stacking to combine outputs effectively."
},
{
"id": 91,
"questionText": "Scenario: Base learners are overfitting slightly. Meta-learner underfits. Recommendation?",
"options": [
"Reduce base learner overfitting and slightly increase meta-learner capacity",
"Ignore base learners",
"Train meta-learner on test data",
"Increase dataset size only"
],
"correctAnswerIndex": 0,
"explanation": "Balancing base and meta-learner capacities improves overall stacking performance."
},
{
"id": 92,
"questionText": "Scenario: Stacking regression, meta-learner predicts negative values where base predictions are positive. Fix?",
"options": [
"Check scaling and bias adjustments in meta-learner",
"Ignore predictions",
"Reduce base learners",
"Use training predictions instead of out-of-fold"
],
"correctAnswerIndex": 0,
"explanation": "Meta-learner may require proper scaling or offset to combine base predictions correctly."
},
{
"id": 93,
"questionText": "Scenario: Meta-learner training time is extremely high. Possible solution?",
"options": [
"Reduce number of base learners or use simpler meta-learner",
"Increase base learner complexity",
"Ignore training time",
"Use training predictions directly"
],
"correctAnswerIndex": 0,
"explanation": "Simplifying the meta-learner or reducing base learners can significantly lower computation time."
},
{
"id": 94,
"questionText": "Scenario: Stacking for imbalanced classification. Recommended approach?",
"options": [
"Use probability outputs and apply class weighting or sampling strategies",
"Ignore imbalance",
"Train meta-learner on majority class only",
"Use raw features directly"
],
"correctAnswerIndex": 0,
"explanation": "Meta-learner can be trained with balanced inputs to handle imbalanced datasets effectively."
},
{
"id": 95,
"questionText": "Scenario: Multiple base learners provide continuous outputs with different scales. What is recommended?",
"options": [
"Normalize or standardize outputs before feeding into meta-learner",
"Ignore scale differences",
"Train meta-learner on raw values",
"Use only one base learner"
],
"correctAnswerIndex": 0,
"explanation": "Meta-learner performs better when inputs are on comparable scales."
},
{
"id": 96,
"questionText": "Scenario: Stacking with three classifiers, meta-learner predicts incorrectly on edge cases. Solution?",
"options": [
"Use more diverse base learners or add engineered features",
"Reduce base learner diversity",
"Ignore predictions",
"Train on training predictions only"
],
"correctAnswerIndex": 0,
"explanation": "Meta-learner can improve predictions on edge cases if base learners provide complementary information."
},
{
"id": 97,
"questionText": "Scenario: You stack tree-based models with logistic regression meta-learner. Test RMSE is higher than best base learner. Likely cause?",
"options": [
"Meta-learner overfitted or base predictions too correlated",
"Stacking always reduces RMSE",
"Dataset too large",
"Meta-learner too simple"
],
"correctAnswerIndex": 0,
"explanation": "Correlation among base learners or overfitting in meta-learner can degrade performance."
},
{
"id": 98,
"questionText": "Scenario: Combining heterogeneous models via stacking for regression. Key considerations?",
"options": [
"Diversity, proper meta-learner training, scaling of outputs",
"Use identical base learners only",
"Ignore cross-validation",
"Increase number of base learners blindly"
],
"correctAnswerIndex": 0,
"explanation": "Effective stacking requires diverse base learners, out-of-fold meta-learner training, and proper scaling."
},
{
"id": 99,
"questionText": "Scenario: Meta-learner underfits in a classification stacking task. Recommended action?",
"options": [
"Increase meta-learner capacity slightly or add engineered features",
"Reduce base learner diversity",
"Ignore base learners",
"Train meta-learner on training predictions"
],
"correctAnswerIndex": 0,
"explanation": "A slightly more complex meta-learner can capture relationships between base learners’ outputs."
},
{
"id": 100,
"questionText": "Scenario: Stacking regression ensemble shows overfitting. Which step should be prioritized?",
"options": [
"Verify meta-learner uses out-of-fold predictions and apply regularization",
"Add more base learners",
"Ignore overfitting",
"Train meta-learner on full training predictions"
],
"correctAnswerIndex": 0,
"explanation": "Out-of-fold predictions and regularization are essential to prevent overfitting in stacking ensembles."
}
]
}
|