10 Advanced Machine Learning MCQs with Answers & Explanations (Generative vs Discriminative, KDE, Boosting, k-NN)

✔ Scroll down and test yourself — answers are hidden under the “View Answer” button.

📌 CSE Quizzes Home

• DBMS MCQs Home
• NLP MCQs Home
• ML MCQs Home
• OS MCQs Home

Hidden Markov Model - MCQs - Problem-based Practice Questions

Machine Learning - Advanced MCQs

Understanding the foundations of machine learning requires a strong grasp of how different models learn from data, make predictions, and generalize. This collection of MCQs covers essential concepts such as generative vs discriminative classification, k-NN behavior, MAP vs MLE estimation, boosting dynamics, kernel methods, and decision tree depth—topics frequently asked in exams, interviews, and university courses.

These questions are designed to strengthen conceptual clarity and test real-world intuition about model assumptions, probability distributions, density estimation, and decision boundaries.

Whether you are preparing for GATE, UGC NET, university assessments, data science interviews, or machine learning certifications, this curated set will help you quickly revise key principles and identify common pitfalls in ML theory.

1. In a generative classification model, once you estimate the class-conditional density P(X∣Y) and prior P(Y), the decision rule is obtained by:

A. Minimizing the empirical risk
B. Evaluating P(Y | X) using Bayes’ rule
C. Maximizing the margin between classes
D. Fitting a logistic function to the data

Answer: B
Explanation:

Generative models estimate P(X∣Y) and P(Y). Classification is performed using Bayes’ rule:
P(Y∣X) ∝ P(X∣Y)P(Y).

Generative models are a class of machine learning models that learn the underlying data distribution and can generate new data samples similar to those seen during training.

A generative model learns the joint probability distribution: 𝑃(𝑋,𝑌) or just 𝑃(𝑋). This means the model tries to understand how the data is produced, not just how to classify it.

2. Logistic regression and Gaussian Naive Bayes can produce identical decision boundaries under which condition?

A. When the covariances of all classes are identity and equal
B. When logistic regression is regularized
C. When data is linearly separable
D. When priors are uniform only

Answer: A
Explanation:

GNB with shared identity covariance produces a linear discriminant identical to logistic regression’s functional form.

Understanding the Question

This question asks under which specific condition Logistic Regression (LR) and Gaussian Naive Bayes (GNB) classifiers produce identical decision boundaries. The key is understanding the mathematical relationship between these two seemingly different algorithms.

Both models, Logistic Regression (LR) and Gaussian Naïve Bayes (GNB) normally produce different decision boundaries because:

• LR is discriminative → models 𝑃(𝑌∣𝑋). That is, Logistic Regression directly models the conditional probability 𝑃(𝑌∣𝑋) using the logistic function.
• GNB is generative → models 𝑃(𝑋∣𝑌). That is, Gaussian Naive Bayes is a generative classifier that models the joint probability 𝑃(𝑋,𝑌) by estimating P(Y) and 𝑃(𝑋∣𝑌).

But under a special condition, they produce identical linear decision boundaries. That special condition is: When the covariances of all classes are identity and equal.

When GNB has: Identity covariance, that means, no correlation between features, each feature has variance=1 and same covariance for each class, Gaussian Naïve Bayes's decision boundary has the same mathematical form as Logistic Regression.

Both models produce: 𝑤^⊤𝑋+𝑏=0. Same functional form, so same separating hyperplane.

3. Which of the following best explains why the training error of 1-NN is always zero?

A. Because 1-NN memorizes the class means
B. Because each training point is its own nearest neighbor
C. Because 1-NN uses leave-one-out validation
D. Because 1-NN normalizes distances

Answer: B
Explanation:

Each training sample is its own closest neighbor, so 1-NN always predicts correctly on training data.

What is 1-NN?

1-NN means 1-Nearest Neighbor, which is the simplest form of the k-Nearest Neighbors (k-NN) algorithm. 1-NN classifier assigns the class of a new point based on the single closest training point in the dataset.

Why is the training error of 1-NN always zero?

In 1-Nearest Neighbor classification, when predicting the label of a data point, the algorithm finds the closest point in the dataset. But if you test 1-NN on the same training data, then every training point’s nearest neighbor is itself (distance = 0). So the classifier simply returns its own label, which is always correct.

Thus: Training Error = 0, because no point is misclassified when it's compared with itself.

4. For which type of prior does the MAP estimate not converge to the MLE even with infinite data?

A. Gaussian prior
B. Beta prior
C. A prior that assigns probability 1 to a single parameter value
D. Uniform prior

Answer: C
Explanation:

A degenerate (a.k.a. point-mass or delta) prior forces the parameter to a fixed value regardless of data, so MAP ≠ MLE even with infinite samples.

5. Cross-validation is useful in boosting primarily because:

A. Boosting has no natural stopping point
B. Boosting inherently underfits
C. Boosting does not use loss functions
D. Boosting requires validation to update weights

Answer: A
Explanation:

Boosting can overfit if allowed to run indefinitely. CV selects the optimal number of rounds.

Boosting keeps improving training accuracy indefinitely and can easily overfit, so cross-validation is needed to decide how many boosting steps to perform.

What is boosting?

Boosting is a family of ensemble learning techniques that turn a collection of weak learners (models that are only slightly better than random guessing) into a single strong learner with high predictive accuracy. The core idea is simple: train models sequentially, each one focusing on the mistakes made by the previous ones, and then combine their predictions (usually by a weighted vote or sum). By doing this, the ensemble corrects its own errors over time and ends up far more powerful than any individual component.

What is cross-validation?

Cross-validation is a fundamental resampling technique used to evaluate machine learning models' ability to generalize to unseen data while preventing overfitting. It works by systematically partitioning the dataset into multiple subsets (called folds), training models on some subsets, and testing on others, with this process repeated multiple times to obtain a reliable performance estimate.

Why does boosting need cross-validation?

Boosting algorithms (like AdaBoost, Gradient Boosting, XGBoost, etc.) build models sequentially, adding weak learners (usually decision stumps/trees) one at a time.

Unlike many other models:

• There is no built-in rule that tells you when to stop adding more learners.
• If you keep boosting longer, the model can overfit heavily.

So, to choose the right number of boosting rounds, we use cross-validation. Cross-validation helps to decide: How many weak learners give the best performance without overfitting?

This is why libraries like XGBoost include a parameter like early_stopping_rounds, which depends on a validation set.

6. Kernel Density Estimation (KDE) differs from kernel regression because:

A. KDE estimates a probability density; kernel regression estimates a function value
B. KDE uses only Gaussian kernels
C. Kernel regression cannot use kernels
D. KDE requires class labels

Answer: A
Explanation:

KDE estimates P(X), while kernel regression estimates the functional relationship ŷ(x) via weighted averages.

Differences between KDE and Kernel regression

What each method estimates/answers:

• KDE answers "what is the probability density?" (it answers, 'how are the data distributed?')
• Kernel regression answers "what is the function value or conditional expectation?" (it answers, 'Given X, what is Y?')

How kernels are used?

• KDE uses kernels to smooth the estimated probability distribution,
• Kernel regression uses kernels to perform weighted local averaging to estimate a conditional relationship between variables.

When to use?

• Use Kernel Density Estimation when you want to understand how the data is distributed, especially when you do NOT assume the distribution is normal. Example: Estimate the density of customer ages
• Use kernel regression when you want to predict Y from X in a non-parametric, smooth way.

Supervised vs Unsupervised

• KDE is unsupervised
• Kernel regression is supervised

7. Boosting a set of weak learners generally produces a classifier whose decision boundary is:

A. Identical to that of each weak learner
B. A weighted combination that can be more complex
C. Always linear, regardless of weak learner type
D. Equivalent to a decision tree of depth 1

Answer: B
Explanation:

Boosting aggregates many weak rules, often resulting in highly nonlinear decision boundaries.

How does boosting affect the complexity of the final decision boundary?

Boosting (e.g., AdaBoost, Gradient Boosting) works by combining many weak learners, typically simple classifiers like decision stumps (depth-1 trees). Each weak learner itself has a simple decision boundary.

But boosting does not just average them; it takes a weighted combination based on each learner’s accuracy. Adding many simple boundaries creates a final decision boundary that can be very complex, often highly nonlinear.

This happens because each new weak learner focuses on misclassified points from previous learners, gradually bending the overall decision surface.

8. Which scenario correctly describes how a decision tree can exceed the number of training samples in depth?

A. When each feature is continuous
B. When many features repeat but labels differ
C. When the impurity measure is entropy
D. When pruning is disabled

Answer: B
Explanation:

If identical feature vectors map to conflicting labels, the tree keeps splitting and can exceed depth n.

Why can a decision tree have depth greater than the number of training samples?

Because depth counts the number of splits along a path, not the number of unique samples or unique feature values. Even if features repeat, the tree keeps splitting as long as it can reduce impurity—possibly creating long chains of binary splits, each separating a subset of samples, even if they have identical feature values.

Why does this happen with repeated features?

When features repeat across multiple samples:

• The tree must use the same features repeatedly to separate conflicting labels.
• Each split on a feature that has been previously split becomes less efficient at separating classes.
• The tree exhibits overfitting behavior, attempting to memorize individual samples rather than learn generalizable patterns.
• If samples are identical in their selected features but have different labels, the tree becomes unable to achieve purity through feature thresholds alone

Decision trees try to make leaves pure. If purity is impossible, depth grows uncontrollably. This is why real systems use: max_depth, min_samples_split, min_samples_leaf.

To avoid pathological overfitting trees.

Example:

When the feature values are repeated (e.g. many rows have x = 5) but the labels differ, the tree may keep trying thresholds that slice right at the repeated value. If the algorithm does not enforce a “strictly decreasing impurity” condition, it could accept a split that leaves the dataset unchanged on one side.

9. In k-NN classification, which statement best explains why increasing k (while keeping the dataset fixed) can improve test performance, especially in noisy datasets?

A. Larger k reduces sensitivity to noise by averaging over more neighbors
B. Larger k forces the classifier to become linear
C. Larger k always guarantees zero training error
D. Larger k makes the classifier equivalent to a decision tree

Answer: A
Explanation:

When k increases, the prediction is based on a majority vote over a larger set of neighbors, which reduces the influence of mislabeled or noisy points. This typically improves generalization by lowering variance, although extremely large k can lead to underfitting.

Larger k reduces sensitivity to noise by averaging over more neighbors.

• Averaging = majority vote – By looking at several nearby points instead of just one, the classifier “averages” their labels. If a few of those neighbours are mislabeled (or are outliers), they are unlikely to dominate the vote.
• Noise reduction – Random fluctuations in the training labels act like noise. Majority voting behaves like a low‑pass filter: it suppresses high‑frequency (noisy) variations while preserving the underlying signal.
• Result on test error – Lower variance ⇒ the learned decision surface is more stable on unseen data, so test error typically goes down (up to a point; if k becomes too large, bias dominates and performance can deteriorate).

Thus, averaging over more neighbours mitigates the effect of noisy or atypical training points, which is why test performance usually improves.

10. Which statement about generative vs discriminative models is correct?

A. Generative models always achieve lower error
B. Discriminative models directly model P(Y∣X)
C. Generative models require fewer assumptions
D. Discriminative models estimate P(X∣Y)

Answer: B
Explanation:

Discriminative models learn P(Y∣X) or direct decision boundaries. Generative models learn P(X,Y).

Model Validation in Machine Learning – 10 HOT MCQs with Answers

✔ Scroll down and test yourself — answers are hidden under the “View Answer” button.

📌 CSE Quizzes Home

• DBMS MCQs Home
• NLP MCQs Home
• ML MCQs Home
• OS MCQs Home

Model Validation in Machine Learning – 10 HOT MCQs with Answers | Cross-Validation, Hold-Out & Nested CV Explained

1. A data scientist performs 10-fold cross-validation and reports 95% accuracy. Later, they find that data preprocessing was applied after splitting. What does this imply?

A. Accuracy is still valid
B. Accuracy may be optimistically biased
C. Folds were too small
D. It prevents data leakage

Answer: B

Explanation: Preprocessing after splitting can leak info from validation folds into training folds, inflating accuracy. That is, preprocessing after splitting can systematically overestimate model performance due to data leakage.

When data preprocessing—such as scaling, normalization, or feature selection—is applied after splitting (i.e., on the entire dataset before dividing into folds), information from the validation/test set can inadvertently leak into the training process. This leakage inflates the measured performance, causing results like the reported 95% accuracy to be higher than what the model would achieve on truly unseen data. This is a well-known issue in cross-validation and machine learning validation.

Correct procedure of data preprocessing in cross-validation

Proper practice is to split the data first, then apply preprocessing separately to each fold to avoid biasing results.

For each fold:

1. Split → Training and Validation subsets

2. Fit preprocessing only on training data

3. Transform both training and validation sets

4. Train model

5. Evaluate

2. Which validation strategy most likely overestimates model performance?

A. Nested cross-validation
B. Random train/test split without stratification
C. Cross-validation on dataset used for feature selection
D. Stratified k-fold

Answer: C

Explanation: Feature selection before CV leaks validation data info, inflating scores. If you perform feature selection on the entire dataset before cross-validation, the model has already “seen” information from all samples (including what should be test data).

• This causes data leakage,
• which makes accuracy look higher than it truly is,
• hence the performance is overestimated.

More explanation: This happens because when feature selection is carried out on the entire dataset before performing cross-validation, information from test folds leaks into the training process. This makes accuracy estimates unrealistically high and not representative of unseen data. Feature selection should always be nested inside the cross-validation loop — i.e., done within each training subset.

3. After tuning using 5-fold CV, how should you report final accuracy?

A. CV average
B. Retrain on full data and test on held-out test set
C. Best fold score
D. Validation score after tuning

Answer: B
Explanation: Always test final tuned model on unseen test set.

The correct way to report final accuracy after tuning using 5-fold cross-validation (CV) is to retrain the model on the full dataset with the best found hyperparameters and then evaluate it on a held-out test set.

What is 5-fold cross validation?

5-fold cross-validation is a technique used to estimate model performance and tune hyperparameters during the development phase. It divides the training data into 5 folds, trains the model 5 times (each time using 4 folds for training and 1 fold for validation), and averages the results to get a more robust performance estimate.

Steps

5-fold CV is used to estimate the model's performance and tune hyperparameters without overfitting to the training data. The accuracy scores obtained from each fold are averaged (CV average) to estimate expected model performance, but these scores are based on training subsets and cannot be considered final. After selecting the best hyperparameters from CV, you typically retrain the model on the entire training data to leverage all available data. The final accuracy should then be reported based on evaluation on a separate held-out test set that was not used in any training or validation to provide an unbiased estimate of performance.

Why Retrain on Full Data while using cross-validation?

Training on all available data maximizes the information the model learns from and typically results in better performance than models trained on only 80% of the training data.

Why Use a Held-Out Test Set?

A separate test set ensures you have an unbiased estimate of how the model will perform on truly unseen data. If you report CV scores as final accuracy, you're reporting performance on data that was used (indirectly) in tuning decisions, which can lead to optimistic estimates.

4. Why might Leave-One-Out CV lead to high variance?

A. Too little training data
B. Needs resampling 
C. Fold too large
D. Almost all data used for training

Answer: D
Explanation: Small change in one sample affects result → high variance.

What is LOOCV?

In Leave-One-Out Cross-Validation (LOOCV), we use n folds, where n = number of samples. For each iteration, we train the model on n − 1 samples and test it on the single remaining sample. This is repeated n times and the results are averaged.

Why is it high variance?

Each training set is almost the same with only one sample changing between folds. That means the model sees nearly all the data each time, so each trained model is very similar, but each test case (the one left-out point) can cause a big swing in the error if the model slightly mispredicts it.

As a result, the estimated performance for each fold fluctuates heavily depending on which single observation is left out. When you average them, the mean may still vary a lot between datasets — hence high variance in the performance estimate.

Example

Suppose you have a dataset of 100 SUVs (Toyota Fortuner, Volkswagen Taigun, Mercedes-Benz GLS, etc.). You train your model 99 times, each time leaving one SUV out as the test case. For the first run you train on 99 SUVs and test on 1 (say, a Range Rover Evoque), then repeat for all 100 cars so every car gets to be the “left-out” test case once.

Most SUVs in your dataset might be mid-range models (₹20–30 lakhs), but a few might be luxury SUVs (like a Range Rover at ₹90 lakhs). Because LOOCV tests on just one car at a time, if that single car happens to be a rare or unusual model (e.g., the only electric SUV), or has outlier features (very high horsepower, unique brand, etc.), the model trained on the other 99 cars may not generalize well to that one.

That single prediction will produce a large error, which strongly affects that fold’s test score. The average of these highly variable fold scores becomes unstable — small changes in the dataset (or presence/absence of a few outliers) can lead to large changes in the reported CV score.

5. When should Time Series CV be used?

A. Independent samples
B. Predicting future from past
C. Imbalanced data
D. Faster training

Answer: B

Explanation:

Time Series CV preserves temporal order to avoid lookahead bias. Use Time Series Cross-Validation when the data have a temporal order, and you want to predict future outcomes from past patterns without data leakage.

Time Series Cross-Validation (TSCV) is used when data points are ordered over time — for example, stock prices, weather data, or sensor readings.

• The order of data matters.
• Future values depend on past patterns.
• You must not shuffle the data, or it will leak future information.

Unlike standard k-fold cross-validation, TSCV respects the chronological order and ensures that the model is trained only on past data and evaluated on future data, mimicking real-world forecasting scenarios.

6. Performing many random 80/20 splits and averaging accuracy is called:

A. Bootstrapping
B. Leave-p-out
C. Monte Carlo Cross-Validation
D. Nested CV

Answer: C

Explanation: Monte Carlo validation averages performance over multiple random splits.

Monte Carlo Cross-Validation (also known as Repeated Random Subsampling Validation) involves randomly splitting the dataset into training and testing subsets multiple times (e.g., 80% training and 20% testing).

The model is trained and evaluated on these splits repeatedly, and the results (such as accuracy) are averaged to estimate the model's performance.

This differs from k-fold cross-validation because the splits are random and may overlap — some data points might appear in multiple test sets or not appear at all in some iterations.

When is Monte Carlo Cross-Validation useful?

• You have limited data but want a more reliable performance estimate.
• You want flexibility in training/test split sizes.
• The dataset is large, and full k-fold CV is too slow.
• You don’t need deterministic folds.
• The data are independent and identically distributed (i.i.d.).

7. Model performs well in CV but poorly on test set. Why?

A. Too many folds
B. Overfitting during tuning
C. Underfitted model
D. Large test set

Answer: B

Explanation: Repeated tuning on the same cross-validation folds can cause overfitting to validation data. A model that performs well in cross-validation but poorly on the test set is often overfitted to the validation folds. It has learned fold-specific noise or patterns that don’t generalize to unseen data.

When a model performs well on cross-validation (CV) but poorly on the test set, it is often because the model has overfitted to the validation data during hyperparameter tuning. In CV, the model and hyperparameters are repeatedly adjusted to optimize performance on the validation folds. This can lead to a model that is too closely tailored to the specific folds used in CV, capturing noise or patterns that do not generalize outside those folds.

How overfitting might be caused in cross-validation?

1. We use cross-validation (CV) to choose hyperparameters (e.g., best learning rate, number of trees, etc.).
2. Each time, we train and validate the model on different CV folds, and we pick the hyperparameters that give the best CV score.
3. Because many hyperparameter combinations are tried, the final set may end up accidentally tuned to the specific folds used in CV rather than the true data pattern.
4. When we finally test the model on a completely unseen test set, performance drops — showing that the CV score was over-optimistic.

8. Which gives most reliable generalization estimate with extensive tuning?

A. Single 80/20 split
B. Nested CV
C. Stratified 10-fold
D. Leave-One-Out

Answer: B

Explanation: Nested CV separates tuning and evaluation, avoiding bias. When you perform extensive hyperparameter tuning, use Nested Cross-Validation to get the most reliable, unbiased estimate of true generalization performance.

How does Nested CV handle optimistic bias?

In standard cross-validation, if the same data is used both to tune hyperparameters and to estimate model performance, it can lead to an optimistic bias. That is, the model "sees" the validation data during tuning, which inflates performance estimates but does not truly represent how the model will perform on new unseen data. 

Nested CV solves this by separating the tuning and evaluation processes into two loops: 

• Inner loop: Used exclusively to tune the model's hyperparameters by cross-validation on the training data. 
• Outer loop: Used to evaluate the generalized performance of the model with the tuned hyperparameters on a held-out test fold that was never seen during the inner tuning. 

This structure ensures no data leakage between tuning and testing phases, providing a less biased, more honest estimate of how the model will perform in real-world scenarios. 

When to use Nested Cross-Validation?

Nested CV is computationally expensive. It is recommended especially when you do extensive hyperparameter optimization to avoid overfitting in model selection and get a realistic estimate of true model performance.

9. Major advantage of k-fold CV over simple hold-out?

A. Ensures higher accuracy
B. Eliminates overfitting
C. Uses full dataset efficiently
D. Requires less computation

Answer: C
Explanation: k-fold cross-validation allows each data sample to serve as both training and validation data, making efficient use of the entire dataset.

Other advantages include:

• Provides a more reliable estimate of model performance.
• Reduces variance in model evaluation compared to a single train/test split.
• Ensures better use of limited data when datasets are small.

10. What best describes the purpose of model validation?

A. Improve training accuracy
B. Reduce dataset size
C. Reduce training time
D. Measure generalization to unseen data

Answer: D
Explanation: Validation estimates generalization performance before final testing.

Go to TOP 10 MCQs in Machine Learning - Home page

Top Machine Learning MCQs with Answers | AI, Data Science & Python Interview Questions

Introduction:

Welcome to the complete index of Machine Learning MCQs with Answers — your one-stop resource for quick revision, interview preparation, and AI certification practice. This page organizes topic-wise MCQs on essential concepts such as Python for Data Science, Supervised and Unsupervised Learning, Support Vector Machines (SVM), Decision Trees, Deep Learning, Regression, Feature Selection, and Model Evaluation. Whether you are preparing for a Machine Learning interview, pursuing a Data Science certification course, or exploring online AI training, these quizzes will strengthen your theoretical and practical knowledge. Bookmark this page for continuous updates and new question sets covering the latest AI, SQL, and Python optimization techniques.

Machine Learning training MCQs

• Training of machine learning models - Gradient descent, optimization

Machine Learning testing MCQs

• Testing of machine learning models - accuracy, generalization

Linear regression MCQs

• Linear regression - Concepts quiz

• Linear regression - Technical quiz

• Linear regression - Python technical quiz

Decision tree MCQs

• Decision tree - Construction and entropy basics

• Decision tree - Splitting, overfitting, and pruning

• Decision tree - Gain ratio, continuous attributes

Support Vector Machine (SVM) MCQs

• SVM - Concepts

Machine Learning - model validation MCQs

• Cross-validation - cross-validation, hold-out, and nested cv

Neural network MCQs
Testing and evaluation MCQs
Feature selection MCQs
Principal Component Analysis MCQs
Clustering MCQs

 

Top 10 ML MCQs on SVM Concepts (2025 Edition)

✔ Scroll down and test yourself — answers are hidden under the “View Answer” button.

📌 CSE Quizzes Home

• DBMS MCQs Home
• NLP MCQs Home
• ML MCQs Home
• OS MCQs Home

Top 10 New MCQs on SVM Concepts (2025 Edition)

1. Which of the following best describes the margin in an SVM classifier?

A. Distance between two closest support vectors
B. Distance between support vectors of opposite classes
C. Distance between decision boundary and the nearest data point of any class
D. Width of the separating hyperplane

×

Answer: C

Explanation: The margin is the perpendicular distance from the decision boundary to the closest data point (support vector). SVM aims to maximize this margin.

2. In soft-margin SVM, the penalty parameter C controls what?

A. The kernel function complexity
B. The balance between margin width and classification errors
C. The learning rate during optimization
D. The dimensionality of transformed space

×

Answer: B

Explanation: Parameter C determines how much misclassification is tolerated. A large C → fewer violations, smaller margin; a small C → allows more violations, larger margin. 

A Soft Margin SVM is a type of Support Vector Machine that allows some misclassification or margin violations in order to achieve better generalization when data is not perfectly linearly separable. In simple words, Soft Margin SVM finds the best possible separating hyperplane that balances maximum margin and minimum classification error. 

Overfitting vs Underfitting: Large C - risk of overfitting, small C - risk of underfitting.

3. Which of the following statements about the kernel trick in SVM is true?

A. It explicitly computes higher-dimensional feature mappings
B. It avoids computing transformations by using inner products in the feature space
C. It can only be applied to linear SVMs
D. It reduces the number of support vectors required

×

Answer: B

Explanation: The kernel trick enables SVMs to work in high-dimensional spaces without explicitly computing the transformed features. It uses kernel functions to calculate inner products in that space, making non-linear separation computationally efficient. 

What is kernel trick? 

A Soft Margin SVM is a type of Support Vector Machine that allows some misclassification or margin violations in order to achieve better generalization when data is not perfectly linearly separable. In simple words: Soft Margin SVM finds the best possible separating hyperplane that balances maximum margin and minimum classification error.

4. Which step is unique to non-linear SVMs?

A. Feature normalization
B. Slack variable introduction
C. Kernel trick application
D. Margin maximization

×

Answer: C

Explanation: The kernel trick allows mapping non-linearly separable data into a higher-dimensional space without explicitly computing transformations — used only in non-linear SVMs. 

Why kernel trick is unique to non-linear SVMs only? 

The kernel trick is used only in non-linear SVMs because linear SVMs already work directly in the input space — there’s no need to map the data to a higher-dimensional space to make it linearly separable. 

The kernel trick is used only in non-linear SVMs because linear SVMs already find a separating hyperplane in the original space, whereas non-linear data needs implicit mapping to higher dimensions. 

In other words: 

• Linear SVM → data is already separable by a straight hyperplane → no mapping needed. 

• Non-linear SVM → data is not separable in the original space → kernel trick is applied to find a linear boundary in a higher-dimensional feature space. 

5. If the data is perfectly linearly separable, what is the ideal value of C?

A. Very small (close to 0)
B. Moderate (around 1)
C. Very large (→ ∞)
D. Exactly equal to margin value

×

Answer: C

Explanation: A large C ensures no margin violations (hard-margin SVM), which is suitable when data is perfectly separable.

What are the problems with very large C value?

The SVM with very large C value will try to classify every training point correctly, no matter how narrow or overfitted the margin becomes. This lead to;

• Very small margin (The model sacrifices margin width to perfectly fit all training points.)
• Overfitting risk (It fits even noisy or outlier points, harming generalization to new data.)
• Hard margin behavior (the Soft Margin SVM effectively becomes a Hard Margin SVM, demanding perfect separation.)

Unstable model (Small changes in data may cause large shifts in the decision boundary)

6. Which optimization problem does SVM solve during training?

A. Minimization of loss function via gradient descent
B. Maximization of likelihood function
C. Quadratic optimization with linear constraints
D. Linear programming without constraints

×

Answer: C

Explanation: SVM training is a quadratic optimization problem where a convex quadratic function is minimized under linear constraints. 

Why the optimization problem is "Quadratic optimization with linear constraints"? 

During SVM training, the goal is to find the best separating hyperplane between two classes. That means we need to find w and b such that the classifier f(x) = w⋅x + b correctly separates the classes with the largest possible margin. 

The objective is to maximize the margin (2/||w||). To maximize this margin, we need to minimize L2 norm squared of the weight vector w (i.e. ∣∣w∣∣²). So the optimization problem becomes a quadratic function of w (i.e., ∣∣w∣∣²/2 ). Hence the name quadratic optimization. 

To ensure correct classification, every data point (x_i, y_i) must satisfy: y_i(w.x_i + b) >= 1. This means: 

• Points of the positive class lie on one side of the hyperplane. 
• Points of the negative class lie on the other side, at least one margin unit away. 

These constraints are linear in terms of w and b .

7. What is the primary reason for using a kernel function in SVM?

A. To increase training speed
B. To handle non-linear relationships efficiently
C. To reduce the number of features
D. To minimize overfitting automatically

×

Answer: B

Explanation: Kernels implicitly project input data into a higher-dimensional space where linear separation becomes possible for non-linear relationships.

8. In SVM, support vectors are:

A. All training samples
B. Only samples lying on the margin boundaries
C. Samples inside the margin or misclassified
D. Both B and C

×

Answer: D

Explanation: Support vectors are data points that either lie on the margin boundaries or violate the margin (inside it or misclassified). 

Some points lie exactly on the margin boundary. These are support vectors on the margin. 

Some points may lie inside the margin or even be misclassified (in soft margin SVM). These are support vectors violating the margin.

9. When the gamma (γ) parameter of an RBF kernel is too high, what typically happens?

A. The decision boundary becomes smoother
B. Model generalizes better
C. Model overfits by focusing on nearby points
D. Model underfits with large bias

×

Answer: C

Explanation: High γ makes each point’s influence very localized, leading to an overly complex boundary and overfitting. 

What is gamma (γ) parameter? 

γ (gamma) is a hyperparameter that controls how quickly similarity decreases with distance. 

Small γ - Large influence radius (Each data point affects a wide region, leading to smoother, more general decision boundaries) 

Large γ - Small influence radius (Each data point affects only nearby points, leading to tighter, more complex decision boundaries) .

10. Which of the following metrics is most relevant for evaluating SVM on imbalanced datasets?

A. Accuracy
B. Precision and Recall
C. Log-loss
D. Margin width

×

Answer: B

Explanation: On imbalanced data, accuracy is misleading. Precision and recall (or F1-score) better reflect SVM’s real performance.

For deeper understanding, learners can explore machine learning training with placement opportunities or online SVM courses.

Machine learning specialization courses

SVM interview questions 2025

These questions are ideal for those preparing for machine learning certification exams or AI engineer job interviews.

AI engineer skills and salary

AI engineers with expertise in SVM and deep learning earn competitive salaries in 2025, especially in data-driven industries.

Go to TOP 10 MCQs in Machine Learning - Home page