Learn Before
Build a Simple Baseline First, Then Use Error Analysis to Prioritize Improvements
In a new problem area, it is usually difficult to know in advance which design changes will matter most. A better approach is to get a working baseline model built and trained as soon as possible, even if it is simple. After that, examine the model's mistakes to decide which improvements are most promising, and then refine the system step by step.
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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
Starting Direction in a New Spam-Filtering Project
Advice for Building a First Working AI System
What should a team do first when starting an ML project in a domain it does not know well?
Should you wait to design a perfect machine learning system before building any baseline at all?
Once a baseline model is available, which diagnostic review helps you find recurring failure patterns?
Why is it usually better to start with a simple baseline system before investing in a highly elaborate design?
Error analysis can be done effectively before any first model is built, because it only requires labeled data rather than model predictions.
A practical strategy is to build and train a simple first system quickly, perhaps in just a _____ days.
Match each step in a fast-iteration development process to its purpose.
Order the recommended startup workflow for a new machine learning project
What is the main advantage of building a simple first model before a more sophisticated one?
Can an expert usually identify the best ML approach for a new problem without trying any prototypes?
After building a simple baseline system, the recommended next step is _____ to find the most useful improvements and guide iteration.
Match each machine learning recommendation to the reason it is useful.
Order the steps that support a fast baseline-and-iterate approach.
Why a Strong First Draft Beats Trying to Perfect the Plan
Choosing a Fast Baseline for a Ticket Routing Project
Why Study a Weak Baseline System?