data/Support_Vector_Regression.json

{
  "title": "Support Vector Regression Mastery: 100 MCQs",
  "description": "A comprehensive set of 100 multiple-choice questions designed to teach and test your understanding of Support Vector Regression (SVR), starting from fundamental concepts to advanced topics like kernels, hyperparameter tuning, epsilon-insensitive loss, and real-world scenarios.",
  "questions": [
    {
      "id": 1,
      "questionText": "What is the main goal of Support Vector Regression (SVR)?",
      "options": [
        "To classify data points",
        "To cluster similar data points",
        "To reduce dimensionality",
        "To predict a continuous target variable while ignoring small errors within a margin"
      ],
      "correctAnswerIndex": 3,
      "explanation": "SVR tries to fit a function within a tube (epsilon-insensitive margin) around the true target values, minimizing errors outside the tube."
    },
    {
      "id": 2,
      "questionText": "In SVR, what does the epsilon (ε) parameter represent?",
      "options": [
        "Regularization strength",
        "Width of the margin in which no penalty is given for errors",
        "Learning rate",
        "Kernel type"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Epsilon defines a margin of tolerance where predictions within ε of the true value are not penalized."
    },
    {
      "id": 3,
      "questionText": "Which kernel is commonly used in SVR for non-linear relationships?",
      "options": [
        "All of the above",
        "Polynomial kernel",
        "Linear kernel",
        "RBF (Radial Basis Function) kernel"
      ],
      "correctAnswerIndex": 0,
      "explanation": "SVR can use linear, polynomial, or RBF kernels depending on the nature of the data."
    },
    {
      "id": 4,
      "questionText": "Scenario: SVR applied to a dataset with non-linear trend. Linear kernel used. Observation?",
      "options": [
        "Training error zero",
        "Epsilon ignored",
        "Model performs perfectly",
        "Model underfits, poor predictions"
      ],
      "correctAnswerIndex": 3,
      "explanation": "A linear kernel cannot capture non-linear relationships, leading to underfitting."
    },
    {
      "id": 5,
      "questionText": "Scenario: SVR with RBF kernel applied. Observation: very high gamma. Effect?",
      "options": [
        "Overfitting, model follows data too closely",
        "Epsilon ignored",
        "Underfitting",
        "Training error zero"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High gamma makes the kernel narrow, causing the model to fit noise and overfit the training data."
    },
    {
      "id": 6,
      "questionText": "Scenario: SVR applied to dataset with features in different scales. Observation: model performs poorly. Reason?",
      "options": [
        "Intercept missing",
        "Kernel type wrong",
        "Epsilon too high",
        "SVR is sensitive to feature scaling"
      ],
      "correctAnswerIndex": 3,
      "explanation": "SVR requires feature scaling (standardization/normalization) to perform correctly, especially with RBF or polynomial kernels."
    },
    {
      "id": 7,
      "questionText": "Scenario: SVR applied with linear kernel and C too small. Observation?",
      "options": [
        "Model overfits",
        "Model underfits, wide margin, many points outside epsilon",
        "Intercept removed",
        "Training error zero"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Small C gives weak regularization, allowing a wide margin and underfitting the data."
    },
    {
      "id": 8,
      "questionText": "Scenario: SVR applied with epsilon too large. Observation?",
      "options": [
        "Kernel type irrelevant",
        "Model overfits",
        "Many predictions inside margin, poor accuracy",
        "C ignored"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Large epsilon makes the model insensitive to small deviations, reducing accuracy."
    },
    {
      "id": 9,
      "questionText": "Scenario: SVR with RBF kernel. Observation: gamma too small. Effect?",
      "options": [
        "Overfits",
        "Epsilon ignored",
        "Model underfits, unable to capture complex patterns",
        "Training error zero"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Small gamma produces a wide kernel, leading to underfitting and smooth predictions."
    },
    {
      "id": 10,
      "questionText": "Scenario: SVR applied to dataset with outliers. Observation: model robust if epsilon-insensitive loss used. Why?",
      "options": [
        "C is irrelevant",
        "Outliers always ignored",
        "Errors within epsilon not penalized, reducing influence of small deviations",
        "Kernel type changes automatically"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Epsilon-insensitive loss ignores small deviations, making SVR less sensitive to minor noise."
    },
    {
      "id": 11,
      "questionText": "Scenario: SVR applied on dataset with non-linear trends. Comparison: Linear vs RBF kernel. Observation?",
      "options": [
        "Linear kernel always better",
        "Training error zero",
        "Epsilon irrelevant",
        "RBF performs better on non-linear data"
      ],
      "correctAnswerIndex": 3,
      "explanation": "RBF kernel can capture non-linear patterns, unlike linear kernel."
    },
    {
      "id": 12,
      "questionText": "Scenario: SVR applied with C too high. Observation?",
      "options": [
        "Epsilon ignored",
        "Kernel type irrelevant",
        "Overfitting, model tries to reduce training error aggressively",
        "Underfitting"
      ],
      "correctAnswerIndex": 2,
      "explanation": "High C penalizes errors heavily, making the model fit training points closely and overfit."
    },
    {
      "id": 13,
      "questionText": "Scenario: SVR applied to dataset with standardized features. Observation: model performs well. Why?",
      "options": [
        "Feature scaling ensures fair distance computation in kernel functions",
        "C irrelevant",
        "Epsilon ignored",
        "Intercept removed"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Scaling is crucial because SVR uses distances (especially RBF/polynomial) which are affected by feature scales."
    },
    {
      "id": 14,
      "questionText": "Scenario: SVR applied with polynomial kernel degree 3. Observation: model captures cubic trends. Limitation?",
      "options": [
        "Underfits always",
        "Epsilon ignored",
        "May overfit if degree too high or C large",
        "Intercept removed"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Higher-degree polynomial kernels can model complex trends but may overfit if hyperparameters not tuned."
    },
    {
      "id": 15,
      "questionText": "Scenario: SVR applied with epsilon=0.1. Observation: residuals smaller than 0.1 ignored. Effect?",
      "options": [
        "Overfits all points",
        "Model focuses only on significant errors, reducing sensitivity to noise",
        "Training error zero",
        "C irrelevant"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Residuals within epsilon are not penalized, allowing robustness to small fluctuations."
    },
    {
      "id": 16,
      "questionText": "Scenario: SVR applied to dataset with few samples and high dimensions. Observation: kernel choice critical. Why?",
      "options": [
        "Linear kernel always overfits",
        "C too small",
        "Epsilon irrelevant",
        "High-dimensional kernels like RBF can overfit small samples"
      ],
      "correctAnswerIndex": 3,
      "explanation": "High-dimensional kernels can overfit small datasets; careful kernel selection is needed."
    },
    {
      "id": 17,
      "questionText": "Scenario: SVR applied with RBF kernel. Observation: both C and gamma tuned via grid search. Purpose?",
      "options": [
        "Always minimize training error",
        "Ignore epsilon",
        "Find optimal hyperparameters balancing bias and variance",
        "Remove intercept"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Grid search helps select C and gamma that prevent under/overfitting and optimize generalization."
    },
    {
      "id": 18,
      "questionText": "Scenario: SVR applied with very small epsilon. Observation?",
      "options": [
        "C irrelevant",
        "Underfits",
        "Model tries to fit nearly all points, risk of overfitting",
        "Kernel type irrelevant"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Small epsilon reduces tolerance, making SVR try to fit almost all data points, increasing risk of overfitting."
    },
    {
      "id": 19,
      "questionText": "Scenario: SVR applied to dataset with noisy measurements. Observation: epsilon too small. Effect?",
      "options": [
        "Underfits",
        "Training error zero",
        "Intercept ignored",
        "Model overfits noise"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Small epsilon forces model to fit noisy points, reducing generalization."
    },
    {
      "id": 20,
      "questionText": "Scenario: SVR with RBF kernel. Observation: gamma increased while C fixed. Effect?",
      "options": [
        "Underfits",
        "Model captures fine patterns but may overfit",
        "Training error zero",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Higher gamma makes the kernel more sensitive, fitting data tightly and risking overfitting."
    },
    {
      "id": 21,
      "questionText": "Scenario: SVR applied to dataset with outliers. Observation: large epsilon. Effect?",
      "options": [
        "Model ignores small deviations and is robust to outliers",
        "Overfits outliers",
        "Underfits severely",
        "Training error zero"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Large epsilon reduces sensitivity to small deviations, providing robustness to outliers."
    },
    {
      "id": 22,
      "questionText": "Scenario: SVR applied with polynomial kernel degree=5, C and epsilon tuned. Observation?",
      "options": [
        "Training error zero",
        "Model can capture complex non-linear trends with controlled overfitting",
        "Intercept removed",
        "Underfits always"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Polynomial kernel allows modeling non-linear trends; tuning C and epsilon controls overfitting."
    },
    {
      "id": 23,
      "questionText": "Scenario: SVR applied with linear kernel to a mostly linear dataset. Observation?",
      "options": [
        "Model overfits",
        "Model performs well, simple and interpretable",
        "Epsilon irrelevant",
        "Underfits"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Linear kernel is ideal for linear relationships, providing simplicity and interpretability."
    },
    {
      "id": 24,
      "questionText": "Scenario: SVR applied to dataset with standardized features and epsilon=0.2. Observation?",
      "options": [
        "C irrelevant",
        "Residuals within 0.2 are ignored, reducing sensitivity to minor noise",
        "Overfits all points",
        "Intercept removed"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Epsilon-insensitive loss allows ignoring minor deviations, improving robustness."
    },
    {
      "id": 25,
      "questionText": "Scenario: SVR applied to dataset with varying scales. Observation: without scaling, RBF kernel fails. Reason?",
      "options": [
        "C irrelevant",
        "Linear kernel always fails",
        "Distance-based kernels are affected by feature scales",
        "Epsilon ignored"
      ],
      "correctAnswerIndex": 2,
      "explanation": "RBF kernel depends on Euclidean distance, so unscaled features distort similarity computation."
    },
    {
      "id": 26,
      "questionText": "Scenario: SVR applied to stock price dataset with non-linear trends. Linear kernel used. Observation?",
      "options": [
        "Overfits perfectly",
        "Underfits, poor predictions",
        "Intercept ignored",
        "Training error zero"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Linear kernel cannot capture non-linear trends, leading to underfitting in stock price prediction."
    },
    {
      "id": 27,
      "questionText": "Scenario: SVR applied with RBF kernel to housing dataset. Observation: gamma too high. Effect?",
      "options": [
        "Overfitting, model captures noise",
        "Training error zero",
        "Epsilon ignored",
        "Underfitting"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High gamma makes the kernel too narrow, causing overfitting to training data."
    },
    {
      "id": 28,
      "questionText": "Scenario: SVR applied to dataset with features in different ranges. Observation: model performs poorly. Solution?",
      "options": [
        "Use linear kernel only",
        "Standardize or normalize features",
        "Decrease C",
        "Increase epsilon"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Scaling ensures fair distance calculation for kernel-based SVR models."
    },
    {
      "id": 29,
      "questionText": "Scenario: SVR applied with small C. Observation?",
      "options": [
        "Intercept ignored",
        "Training error zero",
        "Wide margin, underfitting",
        "Overfitting"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Small C allows more points outside the margin, leading to underfitting."
    },
    {
      "id": 30,
      "questionText": "Scenario: SVR applied with very large epsilon. Observation?",
      "options": [
        "Training error zero",
        "Overfits noise",
        "C irrelevant",
        "Model ignores small deviations, accuracy drops"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Large epsilon reduces sensitivity to deviations, decreasing accuracy."
    },
    {
      "id": 31,
      "questionText": "Scenario: SVR applied with polynomial kernel degree=3. Observation: overfitting on small dataset. Solution?",
      "options": [
        "Ignore epsilon",
        "Increase gamma",
        "Reduce degree or tune C and epsilon",
        "Remove kernel"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Reducing polynomial degree or tuning hyperparameters prevents overfitting on small datasets."
    },
    {
      "id": 32,
      "questionText": "Scenario: SVR applied with RBF kernel. Observation: small gamma. Effect?",
      "options": [
        "Overfits",
        "Training error zero",
        "Intercept ignored",
        "Model underfits, too smooth predictions"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Small gamma creates a wide kernel, unable to capture complex patterns, leading to underfitting."
    },
    {
      "id": 33,
      "questionText": "Scenario: SVR applied to time-series dataset. Observation: epsilon too small. Effect?",
      "options": [
        "Training error zero",
        "Intercept ignored",
        "Underfits",
        "Model overfits minor fluctuations"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Small epsilon forces the model to fit almost all data points, including noise."
    },
    {
      "id": 34,
      "questionText": "Scenario: SVR applied with RBF kernel to noisy dataset. Observation: large epsilon. Effect?",
      "options": [
        "Underfits severely",
        "Model ignores small deviations, robust to noise",
        "Overfits noise",
        "Training error zero"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Epsilon-insensitive loss ignores minor deviations, reducing sensitivity to noise."
    },
    {
      "id": 35,
      "questionText": "Scenario: SVR applied with cross-validation on C and gamma. Purpose?",
      "options": [
        "Always minimize training error",
        "Find optimal hyperparameters to balance bias and variance",
        "Remove intercept",
        "Ignore epsilon"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Cross-validation selects the best C and gamma values to improve generalization and prevent over/underfitting."
    },
    {
      "id": 36,
      "questionText": "Scenario: SVR applied to housing dataset with RBF kernel. Observation: predictions very smooth. Reason?",
      "options": [
        "Kernel linear",
        "Gamma too small, wide kernel",
        "Epsilon too small",
        "C too high"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Small gamma causes the kernel to be wide, resulting in smooth underfitting predictions."
    },
    {
      "id": 37,
      "questionText": "Scenario: SVR applied to dataset with standardized features. Observation: improved performance. Why?",
      "options": [
        "C irrelevant",
        "Feature scaling ensures fair distance computation in kernel",
        "Epsilon ignored",
        "Intercept removed"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Scaling is necessary because kernel functions depend on feature distances."
    },
    {
      "id": 38,
      "questionText": "Scenario: SVR applied with RBF kernel. Observation: overfitting. Recommended action?",
      "options": [
        "Decrease gamma or C, increase epsilon",
        "Increase gamma",
        "Remove kernel",
        "Decrease epsilon only"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Reducing gamma or C and increasing epsilon reduces overfitting by simplifying the model."
    },
    {
      "id": 39,
      "questionText": "Scenario: SVR applied to dataset with outliers. Observation: model robust. Reason?",
      "options": [
        "Epsilon-insensitive loss ignores small deviations",
        "C too high",
        "Gamma too small",
        "Kernel linear"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Epsilon-insensitive loss reduces impact of small errors, making SVR robust to noise/outliers."
    },
    {
      "id": 40,
      "questionText": "Scenario: SVR applied with polynomial kernel degree 5, small dataset. Observation: overfitting. Solution?",
      "options": [
        "Ignore epsilon",
        "Increase gamma",
        "Reduce degree or tune C and epsilon",
        "Remove kernel"
      ],
      "correctAnswerIndex": 2,
      "explanation": "High-degree polynomial can overfit; tuning reduces complexity and improves generalization."
    },
    {
      "id": 41,
      "questionText": "Scenario: SVR applied to financial dataset. Observation: linear kernel performs well. Reason?",
      "options": [
        "Linear kernel always best",
        "C irrelevant",
        "Data has mostly linear relationship",
        "Epsilon ignored"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Linear kernel suffices if the underlying relationship is mostly linear."
    },
    {
      "id": 42,
      "questionText": "Scenario: SVR applied with large epsilon. Observation: residuals within margin ignored. Effect?",
      "options": [
        "Model ignores small deviations, reduces sensitivity to noise",
        "Overfits minor fluctuations",
        "Underfits severely",
        "Training error zero"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Residuals within epsilon are not penalized, making SVR robust to minor deviations."
    },
    {
      "id": 43,
      "questionText": "Scenario: SVR applied to time-series with RBF kernel. Observation: gamma too high. Effect?",
      "options": [
        "Overfits, model fits noise",
        "Underfits",
        "Training error zero",
        "Epsilon ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High gamma makes kernel very narrow, overfitting small fluctuations in time-series."
    },
    {
      "id": 44,
      "questionText": "Scenario: SVR applied to dataset with 50 features, 200 samples. Observation: gamma and C tuned via grid search. Advantage?",
      "options": [
        "Remove intercept",
        "Training error minimized only",
        "Optimal bias-variance tradeoff",
        "Ignore epsilon"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Grid search finds hyperparameters that optimize generalization."
    },
    {
      "id": 45,
      "questionText": "Scenario: SVR applied to dataset with noise. Observation: small epsilon. Effect?",
      "options": [
        "Underfits",
        "Overfits noise, poor generalization",
        "C irrelevant",
        "Intercept removed"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Small epsilon reduces tolerance, forcing the model to fit almost all points, including noise."
    },
    {
      "id": 46,
      "questionText": "Scenario: SVR applied with linear kernel to mostly linear dataset. Observation?",
      "options": [
        "Epsilon ignored",
        "Underfits",
        "Good performance, simple and interpretable",
        "Overfits"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Linear kernel works well for mostly linear relationships."
    },
    {
      "id": 47,
      "questionText": "Scenario: SVR applied with RBF kernel, standardized features, tuned C and gamma. Observation?",
      "options": [
        "Overfits always",
        "Model captures non-linear trends accurately",
        "Intercept ignored",
        "Underfits always"
      ],
      "correctAnswerIndex": 1,
      "explanation": "RBF kernel with proper tuning captures non-linear trends effectively."
    },
    {
      "id": 48,
      "questionText": "Scenario: SVR applied with polynomial kernel degree 4. Observation: training error very low but test error high. Reason?",
      "options": [
        "C too small",
        "Overfitting due to high-degree polynomial",
        "Underfitting",
        "Epsilon too large"
      ],
      "correctAnswerIndex": 1,
      "explanation": "High-degree polynomial can fit training data too closely, leading to overfitting."
    },
    {
      "id": 49,
      "questionText": "Scenario: SVR applied to dataset with features of different scales. Observation: model poor. Solution?",
      "options": [
        "Decrease gamma",
        "Increase epsilon",
        "Standardize features",
        "Use linear kernel only"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Feature scaling is essential because SVR uses distances in kernel computation."
    },
    {
      "id": 50,
      "questionText": "Scenario: SVR applied to stock market dataset with RBF kernel. Observation: epsilon-insensitive tube too wide. Effect?",
      "options": [
        "C irrelevant",
        "Training error zero",
        "Model overfits",
        "Many small deviations ignored, poor accuracy"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Wide epsilon tube ignores small deviations, reducing prediction accuracy."
    },
    {
      "id": 51,
      "questionText": "Scenario: SVR applied to high-frequency stock price data. Observation: RBF kernel with very small gamma. Effect?",
      "options": [
        "Epsilon ignored",
        "Training error zero",
        "Overfits noise",
        "Underfits, fails to capture rapid changes"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Small gamma produces a wide kernel, smoothing predictions and missing rapid fluctuations."
    },
    {
      "id": 52,
      "questionText": "Scenario: SVR applied to real estate dataset. Observation: polynomial kernel degree 6 overfits. Solution?",
      "options": [
        "Remove kernel",
        "Reduce polynomial degree or tune C and epsilon",
        "Increase epsilon only",
        "Increase gamma"
      ],
      "correctAnswerIndex": 1,
      "explanation": "High-degree polynomials can model noise; lowering degree or tuning parameters prevents overfitting."
    },
    {
      "id": 53,
      "questionText": "Scenario: SVR applied with RBF kernel, large epsilon, and small C. Observation?",
      "options": [
        "Overfits",
        "Model underfits, ignores small deviations, wide margin",
        "Intercept ignored",
        "Training error zero"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Small C and large epsilon reduce sensitivity to errors, causing underfitting."
    },
    {
      "id": 54,
      "questionText": "Scenario: SVR applied to noisy sensor dataset. Observation: small epsilon, large C. Effect?",
      "options": [
        "Kernel ignored",
        "Underfits",
        "Training error zero",
        "Overfits noise, poor generalization"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Small epsilon and large C force the model to fit almost all points, including noise."
    },
    {
      "id": 55,
      "questionText": "Scenario: SVR applied with RBF kernel on financial data. Observation: gamma tuned via cross-validation. Purpose?",
      "options": [
        "Ignore epsilon",
        "Always minimize training error",
        "Balance bias and variance for better generalization",
        "Remove intercept"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Cross-validation helps find gamma that prevents under/overfitting and improves prediction on unseen data."
    },
    {
      "id": 56,
      "questionText": "Scenario: SVR applied to a dataset with highly correlated features. Observation: performance similar across linear and RBF kernels. Reason?",
      "options": [
        "C too small",
        "Epsilon ignored",
        "Data relationship mostly linear",
        "Linear kernel always best"
      ],
      "correctAnswerIndex": 2,
      "explanation": "When features have linear relationships, both linear and RBF kernels give similar performance."
    },
    {
      "id": 57,
      "questionText": "Scenario: SVR applied to dataset with extreme outliers. Observation: large epsilon. Effect?",
      "options": [
        "Overfits outliers",
        "Training error zero",
        "Underfits completely",
        "Reduces sensitivity to outliers, robust predictions"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Large epsilon ignores small deviations, reducing outlier influence."
    },
    {
      "id": 58,
      "questionText": "Scenario: SVR applied with polynomial kernel degree 5. Observation: low-degree coefficients dominate. Reason?",
      "options": [
        "Epsilon ignored",
        "Overfits noise",
        "High-degree terms penalized by regularization, low-degree terms capture main trend",
        "Training error zero"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Regularization and parameter tuning often shrink high-degree polynomial effects, letting low-degree terms dominate."
    },
    {
      "id": 59,
      "questionText": "Scenario: SVR applied to dataset with 50 features and 200 samples. Observation: gamma and C tuned via grid search. Advantage?",
      "options": [
        "Optimizes bias-variance tradeoff",
        "Epsilon irrelevant",
        "Always minimize training error",
        "Removes intercept"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Grid search selects hyperparameters that generalize best to unseen data."
    },
    {
      "id": 60,
      "questionText": "Scenario: SVR applied to time-series dataset with RBF kernel. Observation: gamma high, C high, epsilon small. Effect?",
      "options": [
        "Overfits training data, poor generalization",
        "Intercept irrelevant",
        "Underfits",
        "Residuals ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High gamma and C with small epsilon make SVR fit almost all points, including noise."
    },
    {
      "id": 61,
      "questionText": "Scenario: SVR applied to housing data with standardized features. Observation: performance improved. Reason?",
      "options": [
        "Linear kernel preferred",
        "Kernel distances computed correctly after standardization",
        "C irrelevant",
        "Epsilon ignored"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Standardization ensures kernel computations are not skewed by feature scales."
    },
    {
      "id": 62,
      "questionText": "Scenario: SVR applied to stock dataset. Observation: predictions smooth, underfitting. Likely cause?",
      "options": [
        "Training error zero",
        "C too high",
        "Kernel linear",
        "Gamma too small or epsilon too large"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Small gamma or large epsilon leads to overly smooth predictions, missing complex trends."
    },
    {
      "id": 63,
      "questionText": "Scenario: SVR applied to dataset with missing values. Observation: training fails. Solution?",
      "options": [
        "Impute or remove missing values",
        "Decrease C",
        "Reduce epsilon",
        "Change kernel"
      ],
      "correctAnswerIndex": 0,
      "explanation": "SVR cannot handle missing values; preprocessing is required."
    },
    {
      "id": 64,
      "questionText": "Scenario: SVR applied to dataset with outliers. Observation: small epsilon, high C. Effect?",
      "options": [
        "Underfits",
        "Overfits outliers",
        "Training error zero",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Small epsilon and high C force the model to fit all points, including outliers."
    },
    {
      "id": 65,
      "questionText": "Scenario: SVR applied with linear kernel to mostly linear data. Observation: performance comparable to RBF. Reason?",
      "options": [
        "C too small",
        "Epsilon ignored",
        "Linear relationship dominant in data",
        "Linear kernel always best"
      ],
      "correctAnswerIndex": 2,
      "explanation": "If the underlying trend is linear, linear and RBF kernels give similar results."
    },
    {
      "id": 66,
      "questionText": "Scenario: SVR applied with polynomial kernel degree 4 on small dataset. Observation: overfitting. Solution?",
      "options": [
        "Remove kernel",
        "Increase gamma",
        "Ignore epsilon",
        "Reduce degree or tune C and epsilon"
      ],
      "correctAnswerIndex": 3,
      "explanation": "High-degree polynomial can overfit small datasets; tuning reduces complexity."
    },
    {
      "id": 67,
      "questionText": "Scenario: SVR applied to dataset with noisy features. Observation: epsilon-insensitive tube helps. Effect?",
      "options": [
        "Training error zero",
        "Overfits all points",
        "Model robust to minor noise, reduces variance",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Epsilon-insensitive loss ignores small deviations, improving robustness."
    },
    {
      "id": 68,
      "questionText": "Scenario: SVR applied to real estate dataset. Observation: small gamma, large epsilon. Effect?",
      "options": [
        "Underfitting, predictions too smooth",
        "Training error zero",
        "Overfitting",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Small gamma widens the kernel, large epsilon ignores deviations, producing smooth underfit predictions."
    },
    {
      "id": 69,
      "questionText": "Scenario: SVR applied to dataset with highly non-linear trends. Observation: RBF kernel tuned well. Effect?",
      "options": [
        "Training error zero",
        "Captures non-linear patterns accurately",
        "Underfits",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Properly tuned RBF kernel models non-linear trends effectively."
    },
    {
      "id": 70,
      "questionText": "Scenario: SVR applied to time-series dataset. Observation: predictions lag behind sudden spikes. Likely cause?",
      "options": [
        "Kernel linear",
        "Epsilon too large, gamma too small",
        "C too high",
        "Training error zero"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Large epsilon ignores small deviations; small gamma smooths predictions, causing lag."
    },
    {
      "id": 71,
      "questionText": "Scenario: SVR applied to standardized financial dataset. Observation: model captures trends well. Reason?",
      "options": [
        "Scaling ensures kernel distances computed correctly",
        "Linear kernel preferred",
        "Epsilon ignored",
        "C irrelevant"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Standardized features prevent distance distortions in kernel computations."
    },
    {
      "id": 72,
      "questionText": "Scenario: SVR applied to dataset with high-dimensional features and few samples. Observation: RBF kernel overfits. Solution?",
      "options": [
        "Increase epsilon",
        "Ignore feature scaling",
        "Decrease C",
        "Reduce gamma or use linear kernel"
      ],
      "correctAnswerIndex": 3,
      "explanation": "High-dimensional kernels can overfit small datasets; reducing complexity helps."
    },
    {
      "id": 73,
      "questionText": "Scenario: SVR applied to housing dataset with polynomial kernel degree 3. Observation: small epsilon improves robustness. Reason?",
      "options": [
        "Epsilon balances sensitivity to minor deviations",
        "Overfits all points",
        "C irrelevant",
        "Training error zero"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Appropriate epsilon allows ignoring small noise while fitting main trends."
    },
    {
      "id": 74,
      "questionText": "Scenario: SVR applied with grid search for C, gamma, epsilon. Observation: selected parameters give best validation performance. Benefit?",
      "options": [
        "Optimal hyperparameters improve generalization",
        "Always minimize training error",
        "Intercept removed",
        "Kernel linear"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Grid search helps select parameters that balance bias and variance for unseen data."
    },
    {
      "id": 75,
      "questionText": "Scenario: SVR applied to financial dataset with RBF kernel. Observation: small epsilon, high C, high gamma. Effect?",
      "options": [
        "Overfits training data, poor generalization",
        "Underfits",
        "Residuals ignored",
        "Intercept irrelevant"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Small epsilon, high C, and high gamma make the model fit almost all points, including noise, causing overfitting."
    },
    {
      "id": 76,
      "questionText": "Scenario: SVR applied to high-frequency trading data. Observation: RBF kernel, gamma extremely high. Effect?",
      "options": [
        "Overfits to noise, poor generalization",
        "Underfits trends",
        "Training error zero",
        "Epsilon ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Very high gamma makes the kernel very narrow, fitting noise and causing overfitting."
    },
    {
      "id": 77,
      "questionText": "Scenario: SVR applied to housing dataset with polynomial kernel degree=7. Observation: model unstable. Solution?",
      "options": [
        "Reduce polynomial degree or tune C and epsilon",
        "Increase gamma",
        "Remove kernel",
        "Increase epsilon only"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High-degree polynomials overfit and produce instability; reducing degree or tuning hyperparameters stabilizes predictions."
    },
    {
      "id": 78,
      "questionText": "Scenario: SVR applied to dataset with missing features. Observation: model fails. Solution?",
      "options": [
        "Impute or remove missing values",
        "Decrease epsilon",
        "Change kernel",
        "Decrease C"
      ],
      "correctAnswerIndex": 0,
      "explanation": "SVR cannot handle missing values; preprocessing is required."
    },
    {
      "id": 79,
      "questionText": "Scenario: SVR applied to noisy stock market data. Observation: small epsilon, high C. Effect?",
      "options": [
        "Overfits noise, poor generalization",
        "Underfits trends",
        "Training error zero",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Small epsilon and high C force the model to fit almost all points, including noise."
    },
    {
      "id": 80,
      "questionText": "Scenario: SVR applied with RBF kernel, gamma too small, epsilon too large. Observation?",
      "options": [
        "Underfits, predictions too smooth",
        "Overfits",
        "Training error zero",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Small gamma and large epsilon make the model insensitive, producing overly smooth predictions."
    },
    {
      "id": 81,
      "questionText": "Scenario: SVR applied to real estate dataset. Observation: grid search used for C, gamma, epsilon. Benefit?",
      "options": [
        "Optimal hyperparameters balance bias and variance",
        "Always minimize training error",
        "Remove intercept",
        "Kernel linear"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Grid search finds the best combination of hyperparameters to generalize well on unseen data."
    },
    {
      "id": 82,
      "questionText": "Scenario: SVR applied to dataset with standardized features. Observation: model improves. Reason?",
      "options": [
        "Kernel distances computed correctly after scaling",
        "Linear kernel preferred",
        "Epsilon ignored",
        "C irrelevant"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Standardization prevents skewing of kernel distance calculations."
    },
    {
      "id": 83,
      "questionText": "Scenario: SVR applied to time-series dataset. Observation: model lags sudden spikes. Likely cause?",
      "options": [
        "Epsilon too large or gamma too small",
        "C too high",
        "Kernel linear",
        "Training error zero"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Large epsilon and small gamma smooth predictions, causing lag behind rapid changes."
    },
    {
      "id": 84,
      "questionText": "Scenario: SVR applied to small dataset with high-dimensional features. Observation: RBF kernel overfits. Solution?",
      "options": [
        "Reduce gamma or use linear kernel",
        "Increase epsilon",
        "Decrease C",
        "Ignore feature scaling"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High-dimensional kernels can overfit small datasets; reducing complexity helps."
    },
    {
      "id": 85,
      "questionText": "Scenario: SVR applied with polynomial kernel degree=4. Observation: training error low, test error high. Reason?",
      "options": [
        "Overfitting due to high-degree polynomial",
        "Underfitting",
        "Epsilon too large",
        "C too small"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High-degree polynomials fit training data too closely, causing poor generalization."
    },
    {
      "id": 86,
      "questionText": "Scenario: SVR applied to financial dataset with RBF kernel. Observation: small epsilon, high C, moderate gamma. Effect?",
      "options": [
        "Model fits closely to significant deviations, balances noise",
        "Underfits",
        "Overfits all points",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Small epsilon and high C make SVR sensitive to important deviations while ignoring minor noise."
    },
    {
      "id": 87,
      "questionText": "Scenario: SVR applied with RBF kernel. Observation: gamma increases, epsilon constant. Effect?",
      "options": [
        "Model becomes more sensitive to small patterns, may overfit",
        "Underfits",
        "Training error zero",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Increasing gamma narrows the kernel, increasing sensitivity to variations and risk of overfitting."
    },
    {
      "id": 88,
      "questionText": "Scenario: SVR applied to dataset with noise. Observation: increasing epsilon. Effect?",
      "options": [
        "Model ignores minor deviations, improves robustness",
        "Overfits all points",
        "Training error zero",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Higher epsilon creates a wider tube, reducing sensitivity to noise."
    },
    {
      "id": 89,
      "questionText": "Scenario: SVR applied with polynomial kernel. Observation: higher-degree terms dominate predictions. Effect?",
      "options": [
        "May overfit, complex curve",
        "Underfits",
        "Training error zero",
        "Epsilon ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High-degree polynomial terms can produce complex predictions and overfitting."
    },
    {
      "id": 90,
      "questionText": "Scenario: SVR applied to dataset with large number of outliers. Observation: large epsilon. Effect?",
      "options": [
        "Model ignores small deviations, robust to noise",
        "Overfits outliers",
        "Underfits completely",
        "Training error zero"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Large epsilon reduces sensitivity to minor deviations, improving robustness against noise and outliers."
    },
    {
      "id": 91,
      "questionText": "Scenario: SVR applied to financial time-series dataset. Observation: gamma and C tuned via grid search. Advantage?",
      "options": [
        "Optimal hyperparameters improve generalization",
        "Always minimize training error",
        "Intercept removed",
        "Kernel linear"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Grid search balances bias and variance by selecting optimal hyperparameters."
    },
    {
      "id": 92,
      "questionText": "Scenario: SVR applied to dataset with standardized features. Observation: model improves. Reason?",
      "options": [
        "Kernel distances computed correctly after scaling",
        "Linear kernel preferred",
        "Epsilon ignored",
        "C irrelevant"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Feature scaling ensures proper kernel distance calculations."
    },
    {
      "id": 93,
      "questionText": "Scenario: SVR applied to time-series dataset. Observation: epsilon too small, C high. Effect?",
      "options": [
        "Overfits minor fluctuations",
        "Underfits",
        "Residuals ignored",
        "Intercept irrelevant"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Small epsilon and high C force SVR to fit almost all points, including noise."
    },
    {
      "id": 94,
      "questionText": "Scenario: SVR applied to dataset with high-dimensional features. Observation: linear kernel better than RBF. Reason?",
      "options": [
        "RBF overfits due to limited samples",
        "Linear kernel always better",
        "Epsilon ignored",
        "C too small"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High-dimensional RBF kernels can overfit when sample size is small; linear kernel is safer."
    },
    {
      "id": 95,
      "questionText": "Scenario: SVR applied with RBF kernel, large epsilon, small C. Observation?",
      "options": [
        "Underfits, wide margin, ignores minor deviations",
        "Overfits",
        "Training error zero",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Large epsilon and small C reduce sensitivity to errors, leading to underfitting."
    },
    {
      "id": 96,
      "questionText": "Scenario: SVR applied to small dataset with polynomial kernel degree 5. Observation: overfitting. Solution?",
      "options": [
        "Reduce degree or tune C and epsilon",
        "Increase gamma",
        "Ignore epsilon",
        "Remove kernel"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High-degree polynomial can overfit; tuning reduces complexity and improves generalization."
    },
    {
      "id": 97,
      "questionText": "Scenario: SVR applied to financial data. Observation: epsilon-insensitive tube reduces minor prediction errors. Benefit?",
      "options": [
        "Robustness to noise, better generalization",
        "Overfits all points",
        "Training error zero",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Epsilon-insensitive loss ignores minor deviations, enhancing robustness."
    },
    {
      "id": 98,
      "questionText": "Scenario: SVR applied with RBF kernel. Observation: gamma too high, epsilon small. Effect?",
      "options": [
        "Overfits noise, poor generalization",
        "Underfits",
        "Training error zero",
        "Intercept ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High gamma and small epsilon make SVR fit closely to every data point, including noise."
    },
    {
      "id": 99,
      "questionText": "Scenario: SVR applied to housing dataset with high feature correlation. Observation: performance similar for linear and RBF kernels. Reason?",
      "options": [
        "Dominant linear relationships reduce kernel differences",
        "Linear kernel always better",
        "Epsilon ignored",
        "C too small"
      ],
      "correctAnswerIndex": 0,
      "explanation": "If relationships are mostly linear, both linear and RBF kernels perform similarly."
    },
    {
      "id": 100,
      "questionText": "Scenario: SVR applied to stock price dataset. Observation: gamma, C, epsilon tuned optimally via grid search. Result?",
      "options": [
        "Accurate predictions, optimal bias-variance tradeoff",
        "Underfits all trends",
        "Overfits all points",
        "Training error zero"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Optimal hyperparameter tuning via grid search ensures good generalization and accurate predictions."
    }
  ]
}