Learn Before
Reviewing About 100 Eyeball Dev Errors Usually Reveals the Main Failure Patterns
Inspecting roughly 100 mistakes by hand is often enough to identify the main categories of errors in an eyeball dev set. If more labeled examples are available, it can still be useful to review additional errors, but the first hundred usually give a strong sense of where the system is failing.
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
How Many Errors to Inspect Before Looking for Patterns?
Reviewing more misclassified examples is never useful once you already have a large dataset.
Baseline Number for _____ Misclassifications
Matching Error-Sample Counts to Meanings
Steps for Deciding on Manual Error Review Size
How many validation errors should be reviewed by hand?
How Many Model Errors Should Be Reviewed?
Main Value of Reviewing 100 Errors
When to Keep Reviewing Additional Errors
Checking About 100 Errors Gives a Useful Error Breakdown