Learn Before
Using Error Counts to Decide Where to Focus Next
Suppose you review 200 misclassified photos from a warehouse robot. Seal-reading mistakes account for 6% of the errors, poor lighting accounts for 14%, and object-occlusion accounts for 25%. If fixing one category perfectly can remove at most that category's share of the total errors, then object-occlusion is the best place to start because it offers the largest possible reduction.
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One Example May Fit Several Error Tags
New Error Categories Can Appear During Review
Choose Error Categories You Can Act On
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?
Learn After
After reviewing 150 misclassified drone-delivery images, package-label errors account for 9% of the mistakes, weather-haze errors account for 18%, and shadow-occlusion errors account for 22%. Which category should you likely prioritize?
If 120 validation errors are reviewed and 9 of them are bicycle mistakes, then fixing bicycle errors perfectly could remove at most 7.5% of the total errors.
Error analysis on 100 misclassified dev set examples shows that traffic-light mistakes can eliminate _____ of the errors at most.
Match each error group to the statement that best describes why it deserves attention.
Put the steps in order for using error-category counts to decide where to focus next.
What does an error category's share of dev-set mistakes tell you?
Should the easiest error category always get top priority in error analysis?
Error-category frequency gives an upper bound on improvement.
Match each error-analysis term with its meaning.
Put the analysis steps in the order that justifies not prioritizing one low-frequency error type.
Explain How Error Category Frequency Guides Project Prioritization
Choosing which error categories to fix first
Greatest Error Reduction From Fixing One Category