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Error Analysis Is an Iterative Process
Error analysis is an iterative process. One may start with no categories in mind, come up with a few ideas after looking at a couple of images, manually categorize some images, think of new categories, and re-examine the images in light of the new categories.
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Machine Learning
Deep Learning
Supervised Learning
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Data Science
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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
Build a Basic System Quickly and Iterate Using Error Analysis
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
Which of the following best describes the nature of error analysis in machine learning, according to Machine Learning Yearning?
True or False: According to Machine Learning Yearning, you must define all error categories before you begin reviewing misclassified examples.
In Machine Learning Yearning, Ng states that after looking at a couple of misclassified images, you might come up with a few ideas for error _____.
Match each phase of the iterative error analysis process to its correct description.
Place the steps of one iterative error analysis cycle in the correct order as described in Machine Learning Yearning.
During error analysis you discover a new error category after completing your first manual categorization pass. What should you do next?
True or False: In Machine Learning Yearning, Ng indicates that discovering new error categories during analysis may require revisiting previously examined examples.
After thinking of new categories during error analysis, you should re-examine examples in light of the _____ categories.
Match each characteristic of the error analysis process to the label that best describes it, as presented in Machine Learning Yearning.
Order the reasoning steps that explain why error analysis benefits from being iterative rather than a single pass.