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Prioritize Actionable Error Categories While Tracking Others
The most useful error buckets are the ones that suggest a realistic improvement. For example, if a photo classifier often fails because product labels cover part of the object, that category is especially valuable when the team has a plausible way to remove the obstruction or make the model more robust to it. Even so, it can still be worthwhile to track some categories you cannot fix immediately, because the main purpose is to learn where the system is weakest and where effort is most likely to pay off.
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One Example May Fit Several Error Tags
New Error Categories Can Appear During Review
Error Review Improves Through Repeated Passes
Using Error Counts to Decide Where to Focus Next
Working on Several Error Buckets at Once
Error Analysis Is Not an Automatic Ranking Rule
A Category's Share of Errors Sets an Upper Bound on Improvement
Error Analysis Helps Estimate Whether a Proposed Change Is Worth the Effort
Why Quick Error Review Is Often Skipped
Incorrect Labels in a Validation Set
Splitting a large development set into a review subset and a tuning subset
Build a Simple Baseline First, Then Use Error Analysis to Prioritize Improvements
Using Training-Set Mistakes to Diagnose High Bias
Reviewing a Sample of Validation Errors
Separating Search Errors from Scoring Errors in Inference
Component-Wise Error Review
Error Analysis as a Data-Science Lens on Model Mistakes
Multiple Valid Approaches to Error Analysis
Tasks Humans Can Perform Give Stronger Error Analysis Benchmarks
When diagnosing a model, what should error analysis focus on first?
Error analysis on a machine learning system must follow one fixed procedure.
Name the practice of reviewing mistakes to understand why predictions failed.
Match each error-analysis idea to the description that best fits it.
Put the steps of a simple dev-set error review in the right order.
Why is it useful to inspect misclassified examples during error analysis, even for error types you cannot immediately repair?
Error analysis is usually repeated after each round of model changes.
Error analysis can help you judge which improvement paths look most _____.
Match each error-analysis activity with the benefit it can provide.
Order the steps for deciding which error types to target after a first pass of error review.
Why Error Review Helps Set the Right Next Priorities
Plan the next review step after repeated image-classifier mistakes.
What is error analysis used for in machine learning?
Prioritize Actionable Error Categories While Tracking Others
Learn After
Which error groups are most useful to add first?
You should only analyze an error category if you already know how to fix it.
Building Intuition on _____ Opportunities
Choosing Useful Error Groups
Adding a Newly Discovered Error Category to Analysis
Why Track Hard-to-Fix Error Categories?
Tracking Filtered Photo Errors
Most useful error categories
Why Use Broad Error Groups in Analysis?
You should prioritize only error categories that already have a guaranteed fix.