Learn Before
Tasks Humans Can Perform Give Stronger Error Analysis Benchmarks
Error analysis is most informative when the system is trying to do something people can also do, because human performance offers a practical reference point. If a model’s outputs or internal subtasks depend on abilities people do not reliably have, some standard error-analysis methods become less useful.
0
1
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
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
Why is error analysis especially useful when the machine learning task is something people can already do reliably?
When people can perform a task reliably, their performance can be used as a reference point for analyzing model errors.
Human-Solvable Tasks and Better _____
Match each task situation to the error-analysis implication it supports.
Using Human Performance as a Benchmark for Error Analysis
What should a team expect when its system solves a problem that experienced people cannot judge reliably?
Error Analysis Always Works the Same Way for Every Task
If an ML system depends on abilities that _____ cannot reliably perform, some standard error-analysis techniques stop being useful.
Match each machine learning scenario to its consequence for error analysis tools.
Order the reasoning steps showing how human-performable tasks support better project prioritization.
Using human performance as a reference in machine learning work
When Human Benchmarks Are Unavailable
Why some error analysis methods need a human baseline