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Error Analysis as a Data-Science Lens on Model Mistakes
Error analysis is like applying data science to an ML system’s mistakes so you can decide what improvement to make next.
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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?
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What is error analysis most similar to in practice?
Error analysis is meant to point to the next improvement action for an ML system.
Studying model mistakes is like using _____ to investigate a system's failures.
Match each machine-learning review concept to its role in the debugging process.
Put the error-analysis workflow in a sensible order.
A product team inspects a batch of wrong answers from a text model, groups the failures into recurring categories, and uses that summary to decide what to fix first. What practice is this?
Error analysis is fully automated and consists only of computing loss values and choosing the smallest one.
What Error Analysis Studies
Match each error-analysis idea to the closest step in a standard analytics workflow.
Put the steps of error analysis in a data-science style order.
Why error analysis is like working with data
Using error review to choose the next improvement
Why analyze model mistakes?