Learn Before
A Small Sample of Dev Errors Can Reveal Major Failure Patterns
Manually checking a modest set of development-set mistakes can quickly point to the main kinds of problems a model is having. For example, a review of about 20 errors may show repeated confusion between two labels, weak performance on rare cases, or inconsistent data. That kind of review gives a rough diagnostic picture, not a complete analysis of every failure mode.
0
1
Tags
Machine Learning
Deep Learning
Machine Learning Strategy
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Yearning @ DeepLearning.AI
Related
A Small Set of Mistakes Can Still Guide Priorities
A Small Sample of Dev Errors Can Reveal Major Failure Patterns
Reviewing About 50 Mistakes Reveals Main Error Sources
Reviewing About 100 Eyeball Dev Errors Usually Reveals the Main Failure Patterns
Lower Error Rates Require Larger Review Sets
Why should the manually reviewed dev subset be large enough?
The rough sizing guidance for an eyeball development set is meant for tasks that people can evaluate reliably.
A validation sample should be large enough to reveal the system's main _____.
Match each example count to the amount of insight it typically provides when reviewing errors by hand.
Order the steps for reviewing a validation set to find the most common model mistakes.
Which example task is used to illustrate a rough Eyeball dev set size recommendation?
A tiny validation sample is enough to uncover every major failure mode in a model.
When manual error review is practical
Match each concept to the best description in a model error-analysis setting.
Decide Whether a Human Review Set Is Large Enough
How should an Eyeball dev set be sized to reveal the main error patterns?
Choose the right size for a review set in an image recognition project.
Purpose of an Eyeball Dev Set for Human-Level Tasks
Learn After
What does reviewing about 20 manually inspected development-set errors usually reveal?
Small manual error reviews can reveal big patterns
Reviewing a small sample of mistakes gives a _____ picture of the main error sources.
Match each part of the manual error review statement with its role.
Order the reasoning path from observed classifier errors to a rough picture of what is going wrong.
What a small manual review can show about model errors
What can a team conclude after checking a small batch of model mistakes?
Why is it helpful to inspect a small batch of validation errors?
What is the main takeaway after inspecting about 20 development-set mistakes?
True or false: Examining 20 mistakes means you have fully identified every source of error in the system.