Attributing Dev Set Errors to Pipeline Components
After applying component-level analysis to misclassified dev-set examples, each error can be attributed to a pipeline component. The resulting component-level error fractions help decide where to focus attention.
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Attributing Dev Set Errors to Pipeline Components
In the perfect input test for a cat detector -> cat breed classifier pipeline, what is used to replace the upstream component's output?
True or False: If the cat breed classifier still misclassifies an image given a hand-labeled perfect bounding box, the cat detector is blamed for the error.
In the perfect input test, if the breed classifier now correctly outputs y=1 given a hand-labeled bounding box, attribute the error to the _____.
Match each perfect input test outcome to the component that should be blamed for the pipeline error.
Order the steps of the perfect input test used to attribute a pipeline error between a cat detector and a cat breed classifier.
Explain why substituting a hand-labeled perfect output isolates which pipeline component is responsible for an error.
Diagnose a pipeline error using the perfect input test on a misclassified dev set image.
What must an engineer create to conduct the perfect input test, and why must it be 'perfect'?
Which conclusion follows if the cat breed classifier misclassifies an image even when given a hand-labeled perfect bounding box?
True or False: The perfect input test can be used to attribute an error to exactly one component in a multi-step pipeline.
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What is the primary purpose of attributing each dev-set error to a pipeline component?
True or False: Component-level analysis on misclassified dev-set examples allows each error to be unambiguously attributed to one pipeline component.
Component-level analysis on misclassified dev-set examples lets you estimate the _____ of errors due to each pipeline component.
What does component-level analysis of misclassified dev-set examples produce?
Component-level analysis on misclassified dev-set images allows you to unambiguously attribute each error to one pipeline component.
Estimating the _____ of errors due to each pipeline component helps decide where to focus attention.
Match each component-level dev-set error attribution concept to its role.
Order the steps for attributing dev-set errors to pipeline components.
What is the primary reason for computing error fractions for each pipeline component?
Component-level error attribution should be applied to every dev-set example, including correctly classified ones.
Component-level analysis lets you _____ each dev-set error to a specific pipeline component.
Match each pipeline analysis activity to the outcome it directly produces.
Order the reasoning steps a team should follow after obtaining component-level error fractions.
Explain how component-level error fractions guide machine learning development focus.
Analyze pipeline error attribution for a multi-stage image classifier.
Describe the direct outcome of component-level analysis on misclassified dev set examples.