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Top 57 SVM Interview Questions
Entry Junior Mid Senior Expert
SVM Theoretical Questions
Entry
Junior
Q3:
What types of SVM kernels do you know?
Junior
Q4:
Name some advantages of SVM
Junior
Q5:
What are some applications of SVMs?
Junior
Junior
Q7:
For N dimensional data set what is the minimum possible number of Support Vectors?
N dimensional data set what is the minimum possible number of Support Vectors? Junior
Q8:
What are Hard-Margin and Soft-Margin SVMs?
Junior
Q9:
What happens when there is no clear Hyperplane in SVM?
Junior
Q10:
What are Support Vectors in SVMs?
Junior
Q11:
What is Hyperplane in SVM?
Junior
Mid Mid Q14:
What is Ranking SVM?
Mid Q15:
What are Polynomial Kernels?
Mid Q16:
Related To: Classification
Provide an intuitive explanation of Linear Support Vector Machines (SVMs)
Related To: Classification Mid Q17:
What are the Convex Hulls?
Mid Mid Mid Q20:
When SVM is not a good approach?
Mid Q21:
What is the difference between a Decision Boundary and a Hyperplane?
Mid Mid Q23:
What are Support Vectors?
Mid Q24:
While designing an SVM classifier, what values should the designer select?
Mid Q25:
What is Quadratic Optimisation Problem in SVM?
Mid Mid Q27:
Related To: Anomaly Detection, Logistic Regression
Compare SVM and Logistic Regression in handling outliers
Related To: Anomaly Detection, Logistic Regression Mid Q28:
What is the difference between Classification and Regression when using SVM?
Mid Q29:
What is the Kernel Trick?
Mid Q30:
What is the role of C hyperparameter in SVM?
Mid Mid Q32:
Related To: Curse of Dimensionality, Dimensionality Reduction
Does linear SVMs suffer from the Curse of Dimensionality?
Related To: Curse of Dimensionality, Dimensionality Reduction Senior Senior Q34:
Related To: Classification, Logistic Regression
Name some advantages of using Support Vector Machines vs Logistic Regression for classification
Related To: Classification, Logistic Regression Senior Senior Q36:
Related To: Data Processing
How does the Algorithm "The 10% You Don't Need" remove the Redundant Data?
Related To: Data Processing Senior Q37:
Related To: Classification
How would you deal with classification on Non-linearly Separable data?
Related To: Classification Senior Senior Q39:
How does the value of Gamma affect the SVM?
Senior Q40:
What are Slack Variables in SVM?
Senior Q41:
Why is the Lagrangian important in SVM?
Senior Q42:
What are C and Gamma (γ) with regards to a Support Vector Machine?
Senior Q43:
What is the Dual Problem?
Senior Senior Q45:
How does the value of C affect the SVM?
Senior Q46:
Is there a relation between the Number of Support Vectors and the classifiers performance?
Senior Q47:
Explain the dual form of SVM formulation
Senior Q48:
What are Radial Basis Function Kernels?
Senior Senior Q50:
What is Structured SVM?
Senior Q51:
Why is SVM not popular nowadays? Also, when did SVM perform poorly?
Expert Expert Q53:
How to select Kernel for SVM?
Expert Q54:
What is Mercer's theorem and how is it related to SVM?
Expert Q55:
Why does SVM work well in practice, even if the reproduced space is very high dimensional?
Expert Q56:
How do you approximate RBF kernel to scale with large numbers of training samples?
Expert SVM Practical Challenges
Expert 
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