Out-of-distribution
Out-of-distribution describes data or inputs whose generating distribution differs materially from the distribution represented in a model’s training data.
Outlier Detection in Python
Data containing observations whose distribution differs from the distribution represented by a model's training data.
- How it is used
- Supports image outlier detection so systems can respond cautiously to objects unlike those seen during training.
- How deep it goes
- Defines the distinction through global and local outliers and discusses image-testing assumptions, without specifying a dedicated OOD detector.
- Examples
- Contrasts a parrot with an unusually photographed dog, and compares bird images drawn from different collections.
- What you can do after
- Evaluate/understand it by separating unfamiliar object types from unusual familiar instances and questioning whether a changed data source establishes OOD status.
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Natural Language Processing in Action, Second Edition
An out-of-distribution example is an input whose relevant data-generating conditions differ from those represented in a model's training distribution.
- How it is used
- Frames unseen names as a practical test of whether language models can generalize beyond memorized examples.
- How deep it goes
- Defines the distribution shift, demonstrates its consequence, and stops short of formal distributional analysis.
- Examples
- Uses original names such as Carlana and Cason, plus character-feature vectors produced by TfidfVectorizer.
- What you can do after
- Build character n-gram features that let a model represent names absent from its training examples.
Read how Natural Language Processing in Action, Second Edition explains out-of-distribution →
Related concepts
- anomalykind of · An anomaly is an observation, behavior, or state that deviates from an expected or typical pattern or condition.
- generalizationaddressed by · Generalization is the extension of a pattern, model, solution, or conclusion beyond the specific examples or cases from which it was derived.
- training datasetcontrasts with · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
- local outliercontrasts with · A local outlier is a data item that differs from other items in its nearby or comparable cluster while belonging to a common overall type.
- outlier detectionaddressed by · Outlier detection is the process of identifying observations that substantially deviate from an expected or prevailing pattern in data.
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