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Error Analysis as Data Science for ML Mistakes
Error analysis on a learning algorithm is like using data science to analyze an ML system?s mistakes in order to derive insights about what to do next.
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Machine Learning
Deep Learning
Supervised Learning
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Data Science
Machine Learning Strategy
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
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
According to ML Yearning, error analysis on a learning algorithm is most analogous to which practice?
The primary goal of error analysis, as described in ML Yearning, is to derive insights about what to do next to improve an ML system.
Carrying out error analysis on a learning algorithm is like using _____ to analyze an ML system's mistakes.
Match each error analysis concept to its role in the data-science analogy described in ML Yearning.
Arrange the steps of the error analysis process described in ML Yearning in the correct logical order.
A practitioner manually reviews misclassified examples to identify dominant failure patterns. Which ML Yearning concept does this represent?
According to ML Yearning, error analysis is best described as a purely mathematical, automated process of computing and minimizing loss functions.
Error analysis examines an ML system's _____ in order to derive insights about what to do next.
Match each ML error analysis element to its parallel concept in a traditional data science workflow.
Arrange the reasoning steps that build the analogy between error analysis and data science, as described in ML Yearning.