AI0-001 Machine Learning and Deep Learning Practice Question
A data scientist is tuning hyperparameters for a support vector machine (SVM) with an RBF kernel. Which two hyperparameters most significantly affect model performance? (Select TWO.)
⚠ Common exam trap
Candidates often mistake kernel-specific hyperparameters (e.g., degree for polynomial, gamma for RBF) for general SVM parameters, selecting options like degree or epsilon without realizing they do not apply to the RBF kernel.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
gamma (kernel coefficient)
Gamma defines the influence of a single training example, with low values meaning a far reach and high values meaning a close reach. C controls the trade-off between achieving a low error on the training data and minimizing the margin, directly impacting overfitting. Together, they are the two most critical hyperparameters for an SVM with an RBF kernel.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
gamma (kernel coefficient)
Why this is correct
gamma determines the radius of influence of support vectors.
- ✗
learning rate
Why it's wrong here
Learning rate is not a hyperparameter for SVM; SVM uses quadratic programming.
- ✗
epsilon (for epsilon-SVR)
Why it's wrong here
Epsilon is used in SVM regression, not classification.
- ✗
degree (for polynomial kernel)
Why it's wrong here
Degree is only relevant for polynomial kernel, not RBF.
- ✓
C (regularization parameter)
Why this is correct
C controls the penalty for misclassification, directly affecting margin.
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Written by Johnson Ajibi, MSc IT Security
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