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Explain why having dev/test sets and a metric is important for a team's workflow.
Question: In a few sentences, explain why having a dev set, test set, and metric in place is important for a machine learning team, based on how it affects their ability to evaluate ideas.
Sample answer: Having dev/test sets and a metric is important because it lets a team measure the performance of each idea they try, such as new algorithm parameters, on the dev set. This quick measurement lets the team see whether they are heading in the right direction and quickly detect which ideas produce small or large improvements. As a result, the team can efficiently decide which ideas to keep refining and which to discard, directing their effort toward the most important changes to the system.
Key points:
- Dev/test sets and a metric allow measuring performance of each idea tried
- This lets the team quickly see if they're heading in the right direction
- It helps detect small or large improvements from an idea
- It helps the team decide what to keep refining and what to discard
Rubric: Full credit: explains that dev/test sets and a metric allow quick measurement of idea performance, enable fast decisions about direction, and help the team decide what to refine versus discard. Partial credit: mentions only one of these effects. No credit: does not connect dev/test sets to evaluating ideas.
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Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
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In one to three sentences, describe how the dev set helps a team decide which ideas to keep refining.
What kind of things might a team try after defining a dev set and test set?
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True or False: Having a dev set and metric allows a team to quickly detect which ideas give small or large improvements.
True or False: Having a dev set and metric allows a team to quickly detect which ideas give small or large improvements.