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{
"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."
}
]
}
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