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{
"title": "Support Vector Machines (SVM) Mastery: 100 MCQs",
"description": "A complete 100-question collection designed to teach and test your understanding of Support Vector Machines — from basic margin intuition to advanced kernel tricks, soft margin optimization, hyperparameter tuning, and real-world scenario applications.",
"questions": [
{
"id": 1,
"questionText": "What does an SVM aim to find in the feature space?",
"options": [
"A random boundary",
"A centroid of all data points",
"A hyperplane that maximizes margin",
"A cluster center"
],
"correctAnswerIndex": 2,
"explanation": "SVM aims to find the optimal separating hyperplane that maximizes the margin between classes."
},
{
"id": 2,
"questionText": "Scenario: SVM is trained on perfectly separable data. Which margin type is used?",
"options": [
"No margin",
"Random margin",
"Soft margin",
"Hard margin"
],
"correctAnswerIndex": 3,
"explanation": "Hard margin SVM is used when data is perfectly linearly separable."
},
{
"id": 3,
"questionText": "Scenario: Data contains overlapping classes. Which SVM variation should be used?",
"options": [
"Decision trees",
"Hard margin",
"Soft margin",
"Polynomial kernel only"
],
"correctAnswerIndex": 2,
"explanation": "Soft margin SVM allows some misclassification to handle overlapping data."
},
{
"id": 4,
"questionText": "What is the primary role of support vectors?",
"options": [
"Maximize dataset size",
"Define the decision boundary",
"Increase margin penalty",
"Reduce dimensions"
],
"correctAnswerIndex": 1,
"explanation": "Support vectors are the critical points that define the position and orientation of the separating hyperplane."
},
{
"id": 5,
"questionText": "Scenario: Linear SVM trained on non-linear data. What is likely?",
"options": [
"Perfect accuracy",
"Underfitting occurs",
"Zero training loss",
"Overfitting occurs"
],
"correctAnswerIndex": 1,
"explanation": "Linear SVMs cannot model non-linear relationships, leading to underfitting."
},
{
"id": 6,
"questionText": "Which kernel function maps data to infinite-dimensional space?",
"options": [
"Linear",
"RBF (Gaussian)",
"Polynomial",
"Sigmoid"
],
"correctAnswerIndex": 1,
"explanation": "The RBF kernel maps data into an infinite-dimensional feature space, enabling complex boundaries."
},
{
"id": 7,
"questionText": "Scenario: SVM with RBF kernel and γ is too large. Effect?",
"options": [
"Acts like linear",
"Overfits training data",
"Fails to converge",
"Underfits"
],
"correctAnswerIndex": 1,
"explanation": "Large γ makes the model focus too much on each point, overfitting the training set."
},
{
"id": 8,
"questionText": "Scenario: SVM trained with small C. What happens?",
"options": [
"Overfits training data",
"Allows more misclassifications",
"Creates zero margin",
"Fails to train"
],
"correctAnswerIndex": 1,
"explanation": "A smaller C allows wider margins and tolerates more errors for better generalization."
},
{
"id": 9,
"questionText": "Scenario: Large C used with noisy data. Effect?",
"options": [
"Reduces kernel complexity",
"Ignores outliers",
"Overfits noise",
"Underfits"
],
"correctAnswerIndex": 2,
"explanation": "A large C emphasizes classification accuracy, possibly overfitting noisy samples."
},
{
"id": 10,
"questionText": "Why is feature scaling critical for SVM?",
"options": [
"To remove duplicates",
"To normalize labels",
"Because SVM depends on distance calculations",
"To convert categorical data"
],
"correctAnswerIndex": 2,
"explanation": "SVM uses dot products and distance metrics; scaling prevents feature dominance."
},
{
"id": 11,
"questionText": "Scenario: Two features have vastly different ranges. What happens if not scaled?",
"options": [
"No impact",
"Better accuracy",
"Model bias towards larger scale feature",
"Faster convergence"
],
"correctAnswerIndex": 2,
"explanation": "Unscaled features distort margin calculations, biasing the model."
},
{
"id": 12,
"questionText": "What is the role of the kernel trick?",
"options": [
"Reduces features",
"Improves feature scaling",
"Maps data to higher dimensions without explicit transformation",
"Normalizes data"
],
"correctAnswerIndex": 2,
"explanation": "Kernel trick lets SVM handle non-linear data efficiently without explicit transformation."
},
{
"id": 13,
"questionText": "Scenario: SVM applied to high-dimensional text data. Best kernel?",
"options": [
"Sigmoid kernel",
"Linear kernel",
"RBF kernel",
"Polynomial kernel"
],
"correctAnswerIndex": 1,
"explanation": "Linear SVMs perform well for high-dimensional sparse data such as text."
},
{
"id": 14,
"questionText": "Scenario: Non-linear boundaries observed. Which kernel is best?",
"options": [
"No kernel",
"RBF kernel",
"Sigmoid kernel only",
"Linear kernel"
],
"correctAnswerIndex": 1,
"explanation": "The RBF kernel can model highly non-linear decision boundaries."
},
{
"id": 15,
"questionText": "What does γ control in an RBF kernel?",
"options": [
"Regularization strength",
"The influence of a single training example",
"Learning rate",
"Loss function type"
],
"correctAnswerIndex": 1,
"explanation": "γ defines how far the influence of a training sample reaches; higher γ = closer reach."
},
{
"id": 16,
"questionText": "Scenario: γ too small in RBF kernel. Effect?",
"options": [
"Zero accuracy",
"Underfits; boundary too smooth",
"Fails to converge",
"Overfits"
],
"correctAnswerIndex": 1,
"explanation": "Small γ makes the model too smooth, underfitting complex patterns."
},
{
"id": 17,
"questionText": "What happens if C=∞ in soft-margin SVM?",
"options": [
"Ignores support vectors",
"Always fails",
"Creates random margins",
"Behaves like hard-margin SVM"
],
"correctAnswerIndex": 3,
"explanation": "When C is very large, SVM tries to classify all points correctly like a hard-margin model."
},
{
"id": 18,
"questionText": "Scenario: SVM used for regression (SVR). What is optimized?",
"options": [
"Epsilon-insensitive loss",
"Huber loss",
"Cross-entropy",
"Hinge loss"
],
"correctAnswerIndex": 0,
"explanation": "Support Vector Regression uses epsilon-insensitive loss for fitting continuous data."
},
{
"id": 19,
"questionText": "What happens if all points lie outside the margin in SVM?",
"options": [
"Margin expands",
"Kernel fails",
"Model complexity increases",
"C ignored"
],
"correctAnswerIndex": 2,
"explanation": "If most points lie outside, the penalty term increases model complexity."
},
{
"id": 20,
"questionText": "Scenario: SVM trained with too many features but few samples. Risk?",
"options": [
"Overfitting",
"Perfect generalization",
"Fast convergence",
"Underfitting"
],
"correctAnswerIndex": 0,
"explanation": "High feature-to-sample ratio leads to overfitting."
},
{
"id": 21,
"questionText": "What does the bias term in SVM represent?",
"options": [
"C penalty",
"The offset of the hyperplane",
"Learning rate",
"The variance"
],
"correctAnswerIndex": 1,
"explanation": "Bias determines how far the decision boundary is from the origin."
},
{
"id": 22,
"questionText": "Scenario: RBF kernel with optimal γ and large C. Expected result?",
"options": [
"Linear decision boundary",
"Overfit training set",
"Underfit",
"Ignore support vectors"
],
"correctAnswerIndex": 1,
"explanation": "Large C and high γ both risk overfitting due to complex boundaries."
},
{
"id": 23,
"questionText": "Why does SVM not work well with large datasets?",
"options": [
"Cannot handle linear data",
"Training time increases quadratically",
"Too few features",
"Memory always freed"
],
"correctAnswerIndex": 1,
"explanation": "SVM training complexity scales poorly with data size."
},
{
"id": 24,
"questionText": "Scenario: SVM applied with polynomial kernel degree=10. What happens?",
"options": [
"Overfits data",
"Linear boundary",
"Underfits",
"No effect"
],
"correctAnswerIndex": 0,
"explanation": "High-degree polynomial kernels can easily overfit."
},
{
"id": 25,
"questionText": "What is hinge loss used for?",
"options": [
"Hyperparameter tuning",
"Feature selection",
"Margin-based classification",
"Regression"
],
"correctAnswerIndex": 2,
"explanation": "Hinge loss is used in SVM to measure margin violations."
},
{
"id": 26,
"questionText": "Scenario: Noisy dataset with overlapping features. Best SVM approach?",
"options": [
"Linear only",
"Soft margin with small C",
"High γ",
"Hard margin"
],
"correctAnswerIndex": 1,
"explanation": "Soft margin and smaller C improve tolerance to noise."
},
{
"id": 27,
"questionText": "Scenario: Model overfits using RBF kernel. Possible fix?",
"options": [
"Remove regularization",
"Reduce γ",
"Increase γ",
"Increase C"
],
"correctAnswerIndex": 1,
"explanation": "Reducing γ smooths decision boundaries to avoid overfitting."
},
{
"id": 28,
"questionText": "Scenario: Data not linearly separable but low-dimensional. Efficient kernel?",
"options": [
"Polynomial kernel (degree 2)",
"Linear",
"RBF kernel",
"Sigmoid"
],
"correctAnswerIndex": 0,
"explanation": "Low-degree polynomial kernels can model slight non-linearities efficiently."
},
{
"id": 29,
"questionText": "Scenario: You use an RBF kernel on data with high dimensionality and little noise. What might happen?",
"options": [
"Good fit if parameters tuned",
"Ignores all kernels",
"Underfits automatically",
"Always overfits"
],
"correctAnswerIndex": 0,
"explanation": "High-dimensional data can work well with RBF kernels if hyperparameters are well-tuned."
},
{
"id": 30,
"questionText": "Scenario: Polynomial kernel used with degree=1. What kernel does this mimic?",
"options": [
"RBF kernel",
"No kernel",
"Sigmoid kernel",
"Linear kernel"
],
"correctAnswerIndex": 3,
"explanation": "A polynomial kernel with degree 1 is equivalent to a linear kernel."
},
{
"id": 31,
"questionText": "Scenario: γ in RBF kernel set to 0.001. What happens?",
"options": [
"Acts as linear kernel",
"Fails to converge",
"Model overfits",
"Model underfits; boundary too smooth"
],
"correctAnswerIndex": 3,
"explanation": "Very small γ makes the RBF behave almost linearly, leading to underfitting."
},
{
"id": 32,
"questionText": "Scenario: Multiclass classification with SVM. Which strategy is used?",
"options": [
"One-vs-Rest or One-vs-One",
"Naive Bayes",
"K-Means",
"Softmax"
],
"correctAnswerIndex": 0,
"explanation": "SVM handles multiclass via one-vs-rest or one-vs-one strategies."
},
{
"id": 33,
"questionText": "Scenario: SVM trained on imbalanced data. What may occur?",
"options": [
"Perfect accuracy",
"Bias toward majority class",
"Bias toward minority",
"Uniform decision boundary"
],
"correctAnswerIndex": 1,
"explanation": "SVM may favor the majority class unless class weights are balanced."
},
{
"id": 34,
"questionText": "How does SVM handle non-linear separations?",
"options": [
"By increasing epochs",
"By removing bias",
"By adding dropout",
"By using kernel functions"
],
"correctAnswerIndex": 3,
"explanation": "Kernels allow SVMs to map data into higher-dimensional spaces to handle non-linearity."
},
{
"id": 35,
"questionText": "Scenario: Large C and large γ chosen for RBF kernel. Expected behavior?",
"options": [
"Overfitting",
"Stable model",
"Underfitting",
"Fails to train"
],
"correctAnswerIndex": 0,
"explanation": "Large C and γ can both cause complex decision boundaries, leading to overfitting."
},
{
"id": 36,
"questionText": "Scenario: You increase C from 1 to 1000. What happens?",
"options": [
"Margin widens",
"Kernel ignored",
"Margin becomes narrower",
"Model underfits"
],
"correctAnswerIndex": 2,
"explanation": "A larger C penalizes misclassifications more, resulting in a narrower margin."
},
{
"id": 37,
"questionText": "Which optimization technique does SVM use to find the best hyperplane?",
"options": [
"Simulated annealing",
"Gradient descent",
"Quadratic programming",
"Stochastic optimization"
],
"correctAnswerIndex": 2,
"explanation": "SVMs use quadratic programming to solve the optimization problem."
},
{
"id": 38,
"questionText": "Scenario: High γ, low C combination. Expected result?",
"options": [
"Complex boundary but tolerates errors",
"Training failure",
"Linear separation",
"Perfect fit"
],
"correctAnswerIndex": 0,
"explanation": "High γ adds complexity, but low C softens penalties, balancing flexibility."
},
{
"id": 39,
"questionText": "Scenario: SVM fails to converge. Possible reason?",
"options": [
"Improper scaling or large C/γ",
"Too few features",
"Low variance",
"Kernel not imported"
],
"correctAnswerIndex": 0,
"explanation": "Unscaled data or extreme parameter values can cause convergence issues."
},
{
"id": 40,
"questionText": "Why does SVM perform well in high-dimensional spaces?",
"options": [
"It uses PCA internally",
"It ignores most features",
"It compresses data",
"It depends on support vectors, not dimensionality"
],
"correctAnswerIndex": 3,
"explanation": "SVM focuses on boundary points (support vectors), not the entire space."
},
{
"id": 41,
"questionText": "Scenario: Features highly correlated. Impact on SVM?",
"options": [
"Minimal impact; still works well",
"Fails to classify",
"Reduces C",
"Doubles training time"
],
"correctAnswerIndex": 0,
"explanation": "SVMs can still work well but may benefit from decorrelation or PCA."
},
{
"id": 42,
"questionText": "Scenario: RBF kernel underfits training data. Possible fix?",
"options": [
"Use linear kernel",
"Remove kernel",
"Decrease γ",
"Increase γ or C"
],
"correctAnswerIndex": 3,
"explanation": "Higher γ or C increases flexibility and reduces underfitting."
},
{
"id": 43,
"questionText": "Scenario: Linear kernel chosen for non-linear data. Expected result?",
"options": [
"Balanced model",
"Underfitting",
"Perfect fit",
"Overfitting"
],
"correctAnswerIndex": 1,
"explanation": "Linear kernels cannot capture complex patterns, leading to underfitting."
},
{
"id": 44,
"questionText": "What happens if all data points are support vectors?",
"options": [
"Underfitting",
"Overfitting",
"No change",
"Perfect generalization"
],
"correctAnswerIndex": 1,
"explanation": "If every point influences the boundary, the model likely overfits."
},
{
"id": 45,
"questionText": "Scenario: You observe slow SVM training on large dataset. What can help?",
"options": [
"Add regularization",
"Increase γ",
"Use linear SVM (LinearSVC)",
"Use higher-degree kernel"
],
"correctAnswerIndex": 2,
"explanation": "LinearSVC is optimized for large-scale linear problems."
},
{
"id": 46,
"questionText": "Scenario: Dataset has noise and outliers. Which SVM parameter to tune?",
"options": [
"C (regularization)",
"Degree",
"γ",
"Bias"
],
"correctAnswerIndex": 0,
"explanation": "Smaller C helps tolerate misclassifications and handle noisy data."
},
{
"id": 47,
"questionText": "Scenario: SVM used on normalized image features. Kernel to start with?",
"options": [
"Polynomial (deg=3)",
"RBF",
"Linear",
"Sigmoid"
],
"correctAnswerIndex": 1,
"explanation": "RBF kernel often performs well on normalized, moderate-dimensional data."
},
{
"id": 48,
"questionText": "Scenario: γ=0.0001 and C=1000. Likely effect?",
"options": [
"Overfitting",
"No convergence",
"Underfitting",
"Optimal fit"
],
"correctAnswerIndex": 2,
"explanation": "Extremely low γ makes decision boundary too simple, underfitting occurs."
},
{
"id": 49,
"questionText": "Scenario: Multiclass SVM classification accuracy drops. Fix?",
"options": [
"Add dropout",
"Use balanced class weights",
"Reduce features",
"Switch to regression"
],
"correctAnswerIndex": 1,
"explanation": "Balancing class weights helps when class imbalance causes bias."
},
{
"id": 50,
"questionText": "Scenario: You test SVM on polynomial kernel degree=6. Observation?",
"options": [
"Underfits large datasets",
"Ignores bias",
"Linearizes output",
"Overfits small datasets"
],
"correctAnswerIndex": 3,
"explanation": "High-degree polynomial kernels often overfit, especially on limited data."
},
{
"id": 51,
"questionText": "Scenario: SVM trained with sigmoid kernel. What does it resemble?",
"options": [
"RBF mapping",
"Decision tree splitting",
"Linear regression",
"Neural network activation function"
],
"correctAnswerIndex": 3,
"explanation": "Sigmoid kernel mimics a neural network activation behavior."
},
{
"id": 52,
"questionText": "What is the dual formulation used for in SVM?",
"options": [
"To reduce memory usage",
"To handle high-dimensional kernels",
"To remove bias term",
"To normalize outputs"
],
"correctAnswerIndex": 1,
"explanation": "Dual formulation helps apply kernel trick efficiently in high-dimensional space."
},
{
"id": 53,
"questionText": "Scenario: SVM used for spam detection (text data). Best kernel?",
"options": [
"Polynomial",
"RBF",
"Sigmoid",
"Linear"
],
"correctAnswerIndex": 3,
"explanation": "Linear kernels are efficient and effective for sparse text data."
},
{
"id": 54,
"questionText": "Scenario: Overfitting in SVM model. Which parameter should be reduced?",
"options": [
"Loss function",
"C or γ",
"Degree",
"Bias"
],
"correctAnswerIndex": 1,
"explanation": "Reducing C or γ simplifies the model and improves generalization."
},
{
"id": 55,
"questionText": "Scenario: Dataset has millions of samples. SVM alternative?",
"options": [
"Sigmoid SVM",
"Stochastic gradient linear classifier",
"Polynomial SVM",
"Decision tree"
],
"correctAnswerIndex": 1,
"explanation": "Large datasets often use SGD-based linear classifiers for scalability."
},
{
"id": 56,
"questionText": "Scenario: You use a kernel not positive semi-definite. What may occur?",
"options": [
"Better accuracy",
"Optimization fails",
"Underfitting",
"Overfitting"
],
"correctAnswerIndex": 1,
"explanation": "Non-PSD kernels can violate convex optimization requirements."
},
{
"id": 57,
"questionText": "Scenario: SVM applied for anomaly detection. Variant used?",
"options": [
"One-Class SVM",
"K-Means",
"SVR",
"Binary SVM"
],
"correctAnswerIndex": 0,
"explanation": "One-Class SVM is designed for novelty or anomaly detection tasks."
},
{
"id": 58,
"questionText": "Scenario: Data contains many irrelevant features. Approach?",
"options": [
"Lower γ",
"Feature selection before SVM",
"Add more kernels",
"Increase C"
],
"correctAnswerIndex": 1,
"explanation": "Feature selection reduces noise and improves SVM performance."
},
{
"id": 59,
"questionText": "Scenario: SVM decision boundary oscillates too much. Cause?",
"options": [
"Linear kernel",
"Large γ",
"Small C",
"Small γ"
],
"correctAnswerIndex": 1,
"explanation": "Large γ makes decision boundaries sensitive to individual samples."
},
{
"id": 60,
"questionText": "Scenario: You tune γ=0.1, C=10 via grid search. Effect?",
"options": [
"Guaranteed overfit",
"Always underfit",
"Improved generalization",
"No change"
],
"correctAnswerIndex": 2,
"explanation": "Grid search helps find the optimal trade-off between bias and variance."
},
{
"id": 61,
"questionText": "Scenario: SVM used with PCA-transformed features. Benefit?",
"options": [
"Faster convergence and less overfitting",
"No benefit",
"Worse accuracy",
"Kernel ignored"
],
"correctAnswerIndex": 0,
"explanation": "PCA reduces redundancy, improving SVM performance and speed."
},
{
"id": 62,
"questionText": "Scenario: RBF kernel accuracy drops on test data. Likely reason?",
"options": [
"Kernel removed",
"Overfitting due to high γ",
"Underfitting",
"Noisy training data"
],
"correctAnswerIndex": 1,
"explanation": "Too high γ causes overfitting, reducing test performance."
},
{
"id": 63,
"questionText": "Scenario: SVM predicts continuous target. Variant?",
"options": [
"Kernel Ridge",
"SVR (Support Vector Regression)",
"Soft-margin SVM",
"Linear SVM"
],
"correctAnswerIndex": 1,
"explanation": "SVR adapts the SVM principle for regression tasks."
},
{
"id": 64,
"questionText": "Scenario: You combine linear and RBF kernels. Effect?",
"options": [
"Error increases",
"Kernel ignored",
"Hybrid decision surface",
"No benefit"
],
"correctAnswerIndex": 2,
"explanation": "Hybrid kernels can model both linear and non-linear relationships."
},
{
"id": 65,
"questionText": "Scenario: SVM model gives different results on same data. Cause?",
"options": [
"Kernel mismatch",
"Scaling issue",
"Different γ",
"Non-deterministic solver or random state"
],
"correctAnswerIndex": 3,
"explanation": "Different random seeds or solvers can yield slightly varying solutions."
},
{
"id": 66,
"questionText": "Scenario: Class imbalance severe (90:10). Recommended?",
"options": [
"Use sigmoid kernel",
"Reduce features",
"Increase C",
"Use class_weight='balanced'"
],
"correctAnswerIndex": 3,
"explanation": "Setting class_weight='balanced' compensates for imbalance."
},
{
"id": 67,
"questionText": "Scenario: SVM on dataset with 10M samples. Efficient library?",
"options": [
"Polynomial kernel",
"Naive Bayes",
"RBF kernel SVC",
"LinearSVC or SGDClassifier"
],
"correctAnswerIndex": 3,
"explanation": "LinearSVC or SGDClassifier scale better for large data."
},
{
"id": 68,
"questionText": "Scenario: High variance SVM results. Remedy?",
"options": [
"Increase γ",
"Use hard margin",
"Use cross-validation and parameter tuning",
"Add noise"
],
"correctAnswerIndex": 2,
"explanation": "Cross-validation stabilizes and selects optimal hyperparameters."
},
{
"id": 69,
"questionText": "Scenario: Feature scaling forgotten before training. Effect?",
"options": [
"Incorrect margin calculation",
"Higher recall",
"Faster training",
"Better accuracy"
],
"correctAnswerIndex": 0,
"explanation": "Unscaled data distorts distance-based calculations in SVM."
},
{
"id": 70,
"questionText": "Scenario: SVM trained with kernel='poly', degree=5. What to expect?",
"options": [
"No margin",
"Underfitting",
"Overfitting",
"Perfect generalization"
],
"correctAnswerIndex": 2,
"explanation": "High-degree polynomial kernels tend to overfit."
},
{
"id": 71,
"questionText": "Scenario: You visualize decision boundary very smooth. Cause?",
"options": [
"High degree",
"Small C",
"Small γ",
"Large γ"
],
"correctAnswerIndex": 2,
"explanation": "Small γ creates smoother, less complex boundaries."
},
{
"id": 72,
"questionText": "Scenario: You want probabilistic outputs from SVM. How?",
"options": [
"Enable probability=True",
"Use RBF kernel",
"Disable scaling",
"Increase C"
],
"correctAnswerIndex": 0,
"explanation": "Enabling probability=True uses Platt scaling to estimate probabilities."
},
{
"id": 73,
"questionText": "Scenario: Training time too high with kernel SVM. Remedy?",
"options": [
"Use linear approximation",
"Add noise",
"Increase C",
"Increase γ"
],
"correctAnswerIndex": 0,
"explanation": "Approximation methods like LinearSVC or kernel approximation speed up training."
},
{
"id": 74,
"questionText": "Scenario: SVM applied on binary imbalanced medical dataset. Recommendation?",
"options": [
"Drop small class",
"Hard margin",
"Random validation",
"Stratified cross-validation"
],
"correctAnswerIndex": 3,
"explanation": "Stratified cross-validation preserves class ratio during validation."
},
{
"id": 75,
"questionText": "Scenario: Model accuracy high on train, low on test. Issue?",
"options": [
"Low variance",
"Overfitting",
"Scaling error",
"Underfitting"
],
"correctAnswerIndex": 1,
"explanation": "Overfitting occurs when SVM learns noise and fails to generalize."
},
{
"id": 76,
"questionText": "Scenario: Feature correlation high, training unstable. Fix?",
"options": [
"Apply PCA before SVM",
"Use high γ",
"Reduce support vectors",
"Increase C"
],
"correctAnswerIndex": 0,
"explanation": "PCA helps reduce correlation and stabilize training."
},
{
"id": 77,
"questionText": "Scenario: You want to visualize margin width. Which SVM attribute?",
"options": [
"kernel_",
"n_iter_",
"coef_ and intercept_",
"support_"
],
"correctAnswerIndex": 2,
"explanation": "Margin width can be computed using coef_ and intercept_ in linear SVM."
},
{
"id": 78,
"questionText": "Scenario: You use polynomial kernel degree=3. What’s effect?",
"options": [
"Fail to train",
"Linear separation",
"Non-linear decision surface",
"Underfitting"
],
"correctAnswerIndex": 2,
"explanation": "Polynomial kernel allows curved decision boundaries."
},
{
"id": 79,
"questionText": "Scenario: SVM used for text sentiment analysis. Kernel?",
"options": [
"Sigmoid",
"Linear",
"RBF",
"Polynomial"
],
"correctAnswerIndex": 1,
"explanation": "Linear kernel works best for high-dimensional sparse text data."
},
{
"id": 80,
"questionText": "Scenario: Decision boundary too sensitive to single points. Cause?",
"options": [
"Balanced class weights",
"Small γ",
"Large γ",
"Small C"
],
"correctAnswerIndex": 2,
"explanation": "Large γ focuses too much on nearby data, making boundary sensitive."
},
{
"id": 81,
"questionText": "Scenario: You combine SVM with bagging ensemble. Benefit?",
"options": [
"No change",
"Overfitting",
"Higher bias",
"Reduced variance"
],
"correctAnswerIndex": 3,
"explanation": "Ensembling multiple SVMs reduces variance and improves generalization."
},
{
"id": 82,
"questionText": "Scenario: You reduce C drastically. Effect?",
"options": [
"Kernel ignored",
"Perfect accuracy",
"Wider margin, higher bias",
"Narrow margin"
],
"correctAnswerIndex": 2,
"explanation": "Smaller C allows more misclassifications, leading to wider margins."
},
{
"id": 83,
"questionText": "Scenario: Dataset scaled incorrectly. Decision boundary looks tilted. Reason?",
"options": [
"Feature scaling inconsistency",
"Large γ",
"Kernel mismatch",
"Small C"
],
"correctAnswerIndex": 0,
"explanation": "Inconsistent scaling distorts feature space, altering boundary shape."
},
{
"id": 84,
"questionText": "Scenario: You use SVM with polynomial kernel on 3D data. Result?",
"options": [
"Underfit",
"Linear separation",
"Over-generalization",
"Non-linear surface fit"
],
"correctAnswerIndex": 3,
"explanation": "Polynomial kernels enable non-linear separation even in 3D."
},
{
"id": 85,
"questionText": "Scenario: You train SVM on very small dataset. Danger?",
"options": [
"High accuracy guaranteed",
"Underfitting",
"Overfitting due to few points",
"Fast convergence"
],
"correctAnswerIndex": 2,
"explanation": "Small datasets can make SVM overfit due to few support vectors."
},
{
"id": 86,
"questionText": "Scenario: SVM kernel parameter γ=1e5. What happens?",
"options": [
"Underfitting",
"Extreme overfitting",
"Stable training",
"No change"
],
"correctAnswerIndex": 1,
"explanation": "Very high γ makes the model memorize data, leading to overfitting."
},
{
"id": 87,
"questionText": "Scenario: SVM hyperplane perfectly separates training points. Danger?",
"options": [
"No bias",
"Perfect generalization",
"Underfitting",
"Overfitting likely"
],
"correctAnswerIndex": 3,
"explanation": "Perfect separation may indicate overfitting unless data is clean."
},
{
"id": 88,
"questionText": "Scenario: You enable shrinking=True in SVC. Effect?",
"options": [
"Faster optimization using heuristics",
"Slower training",
"Lower accuracy",
"No difference"
],
"correctAnswerIndex": 0,
"explanation": "Shrinking heuristic speeds up convergence during optimization."
},
{
"id": 89,
"questionText": "Scenario: High dimensional dataset (10000 features). Kernel?",
"options": [
"RBF",
"Linear",
"Polynomial",
"Sigmoid"
],
"correctAnswerIndex": 1,
"explanation": "Linear kernel is efficient in very high-dimensional spaces."
},
{
"id": 90,
"questionText": "Scenario: Decision boundary too smooth, misclassifying nonlinear data. Fix?",
"options": [
"Increase γ or use RBF kernel",
"Reduce C",
"Change solver",
"Add dropout"
],
"correctAnswerIndex": 0,
"explanation": "Increasing γ adds flexibility to handle complex patterns."
},
{
"id": 91,
"questionText": "Scenario: You visualize few support vectors only. Meaning?",
"options": [
"Training failed",
"Underfits",
"Model generalizes well",
"Overfits"
],
"correctAnswerIndex": 2,
"explanation": "Fewer support vectors indicate a strong, well-generalized boundary."
},
{
"id": 92,
"questionText": "Scenario: SVM with RBF kernel used for face recognition. Why suitable?",
"options": [
"Less computation",
"Captures complex non-linear relationships",
"Linear mapping only",
"Ignores features"
],
"correctAnswerIndex": 1,
"explanation": "RBF kernels are effective for non-linear facial feature mapping."
},
{
"id": 93,
"questionText": "Scenario: Hyperparameter tuning done via grid search. Risk?",
"options": [
"Underfitting",
"Overfitting to validation set",
"Bias error",
"Kernel mismatch"
],
"correctAnswerIndex": 1,
"explanation": "Excessive grid search tuning can overfit to validation data."
},
{
"id": 94,
"questionText": "Scenario: You observe many support vectors even after tuning. Cause?",
"options": [
"Complex data distribution",
"Low γ",
"Simpler boundary",
"Small C"
],
"correctAnswerIndex": 0,
"explanation": "More support vectors imply complex class boundaries."
},
{
"id": 95,
"questionText": "Scenario: Kernel trick purpose?",
"options": [
"Speed training",
"Reduce dimensionality",
"Compute inner products in higher-dimensional space",
"Add noise"
],
"correctAnswerIndex": 2,
"explanation": "Kernel trick implicitly computes high-dimensional mappings efficiently."
},
{
"id": 96,
"questionText": "Scenario: SVM trained with linear kernel on non-linear XOR data. Outcome?",
"options": [
"Good generalization",
"Overfitting",
"Underfitting",
"Perfect accuracy"
],
"correctAnswerIndex": 2,
"explanation": "Linear kernel cannot separate XOR patterns."
},
{
"id": 97,
"questionText": "Scenario: You set tol=1e-10 in SVM. Effect?",
"options": [
"Overfitting",
"Underfitting",
"Faster training",
"Higher precision but slower convergence"
],
"correctAnswerIndex": 3,
"explanation": "Smaller tolerance increases precision but slows convergence."
},
{
"id": 98,
"questionText": "Scenario: C=0.01 and γ=1. What’s the likely behavior?",
"options": [
"Perfect fit",
"Underfitting with soft margin",
"Overfitting",
"Fast overgeneralization"
],
"correctAnswerIndex": 1,
"explanation": "Low C allows large margin and misclassifications, causing underfit."
},
{
"id": 99,
"questionText": "Scenario: RBF kernel used with default params. What’s the effect?",
"options": [
"Ignores margin",
"Fails to train",
"Depends on feature scaling",
"Always best choice"
],
"correctAnswerIndex": 2,
"explanation": "RBF kernel performs well if data is scaled properly."
},
{
"id": 100,
"questionText": "Scenario: SVM’s decision boundary has maximum margin. Why important?",
"options": [
"Increases bias",
"Improves generalization",
"Decreases variance",
"Reduces training speed"
],
"correctAnswerIndex": 1,
"explanation": "Maximizing margin leads to better generalization and robustness."
}
]
}
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