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Using Training-Set Mistakes to Diagnose High Bias
If a model has high bias and is still performing poorly on the training set, reviewing training-set errors can help uncover systematic weaknesses in what the model is learning.
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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
Sample the Worst Training Cases to Find Error Patterns
When is it useful to analyze errors on the training set?
A model with high bias is usually underfitting.
When bias is high, begin error analysis on the _____ set.
Match each diagnostic idea with its meaning in training analysis.
Order the steps for deciding whether to inspect training-set mistakes.
Why Train-Set Error Analysis Matters Under Underfitting
Next Step After a Model Still Fits the Training Set Poorly
Identifying High Bias Through Training Error Review
How does training-set error analysis fit into a high-bias debugging plan?
Training-set mistake analysis can use the same kind of procedure as dev-set mistake analysis.