A marketing analytics team is using an RBF kernel SVM to classify consumers based on a complex dataset. The model in Panel B (Right) uses the following parameters:

C = 1
Gamma = 5

Which of the following parameter sets is most likely used in Panel A (Left)?

Question

A marketing analytics team is using an RBF kernel SVM to classify consumers based on a complex dataset. The model in Panel B (Right) uses the following parameters:

C = 1
Gamma = 5

Which of the following parameter sets is most likely used in Panel A (Left)?

BlackTom AI · Accepted Answer

Gamma in an RBF SVM controls the locality of influence of each support vector (larger gamma = narrower, more local influence → more wiggly, tightly-fitting decision boundaries); C controls the penalty for misclassification (larger C → less regularization → more complex boundaries to avoid training errors). These two effects are the key concepts.

Step-by-step reasoning:
1. Panel B is given as C = 1, Gamma = 5. With Gamma = 5 (relatively large), the decision boundary will hug the data more tightly and show more local wiggles; with C = 1 the model has moderate regularization. The right panel’s dashed boundary therefore appears fairly detailed and locally adaptive.
2. Panel A (left) shows a noticeably smoother, more rounded boundary around the red class compared with the more intricate right-side boundary. A smoother boundary is produced by a smaller gamma (wider Gaussian kernel), because each support vector influences a larger region and the model generalizes more globally.
3. Compare the answer choices: the only choice that reduces complexity relative to Gamma = 5 while keeping the same regularization level (C = 1) is C = 1, Gamma = 3. That lowers gamma (from 5 to 3) and yields the observed smoother boundary.
4. Options with Gamma = 10 would produce even tighter, more jagged boundaries than Gamma = 5, so they don’t match the smoother left plot. Options with C = 3 increase the penalty for errors (less regularization) and would typically produce a more complex boundary than C = 1; the left plot is not more complex than the right, so C = 3 choices are unlikely.

Why the correct answer is correct: lowering gamma from 5 to 3 while keeping C fixed at 1 produces a smoother, less local decision boundary consistent with Panel A. Other options either raise gamma (more wiggles) or raise C (less regularization/complexity), which contradict the visual difference.

Thus the most likely parameters for Panel A are C = 1, Gamma = 3 (matches the provided answer).

A marketing analytics team is using an RBF kernel SVM to classify consumers based on a complex dataset. The model in Panel B (Right) uses the following parameters: C = 1 Gamma = 5 Which of the following parameter sets is most likely used in Panel A (Left)?单项选择题

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