Financial Risk Manager Part 1

Explanation

Underfitting occurs when a model is too simple to capture the underlying patterns and relationships in the data. This results in poor performance on both training and test data because the model fails to learn the true structure of the data.

Overfitting occurs when a model is too complex and learns not only the underlying patterns but also the noise and random fluctuations in the training data. This results in excellent performance on training data but poor generalization to new, unseen data.

Key differences:

• Underfitting: Model is too simple → High bias, low variance
• Overfitting: Model is too complex → Low bias, high variance

Why other options are incorrect:

• Option B: Reverses the definitions - complexity is associated with overfitting, not underfitting
• Option C: Also reverses the definitions and creates confusion
• Option D: While data quantity can influence fitting, the primary distinction is about model complexity, not data volume

The correct answer is A because it accurately describes underfitting as occurring with overly simple models that cannot capture patterns, and overfitting as occurring with overly complex models that fit noise.