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Discovering New Error Categories While Reviewing Examples
Although one may first formulate categories such as Dog, Great cat, and Blurry and then categorize examples by hand, looking through examples will probably inspire new error categories. For example, after seeing that many mistakes occur with Instagram-filtered pictures, one can add a new Instagram column to the spreadsheet. Manually looking at misclassified examples and asking how or whether a human could have labeled the picture correctly will often inspire new categories of errors and solutions.
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Machine Learning
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Related
Examples Can Belong to Multiple Error Categories
Discovering New Error Categories While Reviewing Examples
Most Helpful Error Categories Are Ones You Have Ideas to Improve
Error Analysis Is an Iterative Process
Error Category Frequency Helps Indicate Which Categories to Focus On
Pursuing Multiple Error Categories in Parallel
Error Analysis Does Not Yield a Rigid Priority Formula
Error Category Fraction as a Ceiling on Possible Error Reduction
Error Analysis as a Quantitative Basis for Project Investment Decisions
Engineer Reluctance to Perform Error Analysis Despite Its Low Time Cost
Mislabeled Examples in the Dev Set
Splitting a Large Dev Set into a Manually Examined Subset and a Hands-Off Subset
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Training Set Error Analysis for High Bias
Manually Reviewing 100 Speech Recognition Dev Set Examples
Debugging Inference Algorithms
Error Analysis by Parts
Error Analysis as Data Science for ML Mistakes
No Single Right Way to Perform Error Analysis
Human-Solvable Problems Enable More Powerful Error Analysis Tools
What does error analysis primarily examine to understand an ML system's mistakes?
There is exactly one correct method for conducting error analysis on an ML system.
The process of looking at misclassified examples to understand error causes is called _____.
Match each error analysis concept to its correct description from Machine Learning Yearning.
Order the steps of conducting a basic error analysis on a dev set as described in Machine Learning Yearning.
What is the primary goal of reviewing misclassified examples during error analysis, even in categories you cannot yet fix?
Machine Learning Yearning describes error analysis as an iterative process.
Error analysis can often help you figure out how _____ different improvement directions are.
Match each error analysis activity to the benefit it provides according to Machine Learning Yearning.
Order the reasoning steps for deciding which error categories to pursue after completing an initial error analysis.
Learn After
What commonly happens when you manually examine misclassified examples during error analysis?
Error categories must be fully finalized before you begin examining any misclassified examples.
After noticing many misclassified examples involve Instagram-filtered pictures, you should add a new _____ to the error analysis spreadsheet.
Match each error analysis action to its primary purpose in discovering new error categories.
Order the steps of Ng's iterative error category discovery process during error analysis.
Which question should you ask when reviewing a misclassified image to inspire new error categories and solutions?
The Instagram error category illustrates how manual review can reveal categories absent from the original error analysis framework.
Manually looking at examples that the algorithm _____ and asking whether a human could label them correctly often inspires new error categories.
Match each term from Ng's error analysis framework to its correct description.
Order the reasoning steps a practitioner follows when a new error pattern is spotted during manual example review.