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?