Why can a fully end-to-end model trail a hand-designed pipeline when labeled examples are limited?

When labeled data is scarce, hand-designed features and rules may provide a large share of the system’s useful knowledge.

When labeled examples are scarce, much of the solution may need to come from human _____.

Match each small-data concept with its meaning.

Reasoning About Handcrafted Features With Very Little Data

Why can limited training data favor hand-designed system parts?

Choose where to place prior knowledge in a data-poor prediction task.

Where does most of the model's knowledge come from when data is scarce?

What is the most appropriate design choice when only a very small labeled dataset is available?

A very small training set does not automatically mean every hand-built pipeline will outperform every end-to-end model.