Chapter C21 · Interactive Course
The examples shape the model
Inspect a collection for the faults that matter.
Core Learning Concept
Inspect the parcel dataset before trusting the model’s score. Identify what one independent situation is, how labels were assigned, which conditions are missing, and what use is permitted. Keep related examples together when splitting data, then record the repairs so later results can be interpreted.
What You’ll Learn in This Chapter
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A feature is an input clue
Identify the information available for a prediction.
A feature is information supplied for a prediction, such as parcel weight or destination. It must be available when the prediction is needed. Using an eventual delivery time to predict that same delivery’s duration leaks the answer into the task. Inspect when each candidate feature becomes known.
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What does one example represent?
Choose the correct unit of an example.
One row is not always one independent situation. Five crops of one parcel photo are related examples. If some crops enter training and others testing, the test may be too familiar. Identify the real unit, such as the parcel or person, and use it when designing the split.
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Where did these examples come from?
Identify missing information about collection and permitted use.
Dataset provenance describes where examples came from and how they were collected. It helps you judge coverage, quality, and permitted uses. Being able to download files does not settle every permission question. Record collection methods and applicable terms before relying on the data for a new purpose.
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Wrong labels teach the wrong target
Recognize a labeling error.
A wrong label supplies the wrong learning target. If a fragile parcel is tagged “not fragile,” training may reinforce an unwanted association. Inspect whether mistakes are isolated or caused by ambiguous labeling instructions. Clear definitions and review of disagreement can matter more than simply collecting additional rows.
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Repeated examples can distort the picture
Identify duplication in a training collection.
Duplicates give specific examples excessive influence and can cause evaluation leakage across splits. Account for exact duplicates, near-copies, and derived items to ensure that testing measures genuine performance on distinct situations.
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Who and what are missing?
Identify a coverage gap relative to intended use.
Coverage is relative to intended use. A daylight-only photo collection has a gap if the deployed system must work at night. More daylight photos do not directly fill that gap. Describe required conditions, compare them with the dataset, and seek evidence for the missing situations.
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A shortcut can look like learning
Detect an irrelevant clue that predicts the training label.
Shortcut learning occurs when a model relies on a clue that predicts labels in the dataset but is unsuitable for the intended task. If fragile objects always have a padded background, the background may become the shortcut. Test controlled changes to that clue while preserving the true target.
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Make useful variations of an example
Recognize data augmentation that preserves the intended target.
Data augmentation creates modified versions of examples, such as a small rotation or lighting change. A useful modification preserves the intended target and represents variation the model should handle. Check that assumption for the task: an edit harmless for one label could change the correct answer for another.
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Keep a record of the dataset
Explain why a dataset needs a version and collection notes.
A dataset version identifies the particular collection used in a run. Notes should describe additions, removals, relabeling, collection conditions, and known gaps. Without that record, a changed score is difficult to explain. Reproducible comparisons need both the data version and the evaluation setup.
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Repair the parcel dataset
Find one label problem, one coverage gap and one source or documentation issue in a collection.