In machine learning, what does 'learning as they go' imply?

In machine learning, what does 'learning as they go' imply?

• A. No data needed
• B. Static rules
• C. Models improve with more data
• D. Only trained once

Answer: (C)

Learning as they go means models adapt and improve as new data arrives. This equals online or incremental learning, where you update the model continually or after each data batch, allowing it to capture new patterns and shifting trends over time. That ongoing refinement is the essence of learning as data streams in. This idea fits practical examples like spam filters updating with fresh emails or recommendation systems adjusting to recent user behavior. It’s not about having no data to learn from, nor about fixed, static rules, nor about training just once. Those would prevent continual improvement as more data becomes available.

Learning as they go means models adapt and improve as new data arrives. This equals online or incremental learning, where you update the model continually or after each data batch, allowing it to capture new patterns and shifting trends over time. That ongoing refinement is the essence of learning as data streams in.

This idea fits practical examples like spam filters updating with fresh emails or recommendation systems adjusting to recent user behavior. It’s not about having no data to learn from, nor about fixed, static rules, nor about training just once. Those would prevent continual improvement as more data becomes available.