When dealing with small, noisy datasets typical in early research stages, which approach is often prioritized over raw predictive power?
Answer
Interpretability, often via models like Random Forests or Gaussian Processes.
For small datasets, prioritizing interpretability allows practitioners to gain insight into *why* a model favors certain features, which directly informs targeted next steps, making the learning phase more efficient than blindly optimizing opaque models.
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