Unlabeled data

As explained in Grokking Machine Learning

Data whose observations do not have associated target labels or outcome annotations.

Unlabeled data has no designated target label. Whether a feature counts as a label depends on the question: pet images tagged with species or weight are labeled for those targets, while images without tags are unlabeled. section 2.1

Figure 2.1 Labeled data is data that comes with a tag, or label. That label can be a type or a number. Unlabeled data is data that comes with no tag. The dataset on the left is labeled, and the label is the type of pet (dog/cat). The dataset in the middle is also labeled, and the label is the weight of the pet (in pounds). The dataset on the right is unlabeled.

Compare pet datasets with species labels, weight labels, and no labels. section 2.1

Without labels, a model cannot predict which pet image is a cat or dog. It can still compare images and group similar ones, without knowing what each group represents. section 2.3

Grouping emails

An email dataset described by size and recipient count can be grouped by similarity, revealing categories without predicting a label. section 2.3

How Grokking Machine Learning covers it

How deep it goes

Defines the absence of labels, notes that label status depends on the problem, and illustrates grouping without detailing algorithm internals.

Examples

Uses pet images without type tags and email records described by size and recipient count.

What you can do after

Understand how unlabeled observations can support similarity grouping and data simplification rather than label prediction.

How it connects

Connects unlabeled data to unsupervised learning, clustering, dimensionality reduction, and generative learning.

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Also explained in

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  Outlier Detection in PythonData whose instances do not have assigned target labels or annotations.
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  Natural Language Processing in Action, Second EditionUnlabeled text is textual data that lacks human-provided category, entity, or other annotation labels.
• 

  liveBookmanning.com
  Building LLM Applications with DSPyData examples that lack target outputs or labels.

Compare how 4 books explain unlabeled data →

Related concepts

• datakind of · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.
• unsupervised learningused by · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
• labeled datacontrasts with · Labeled data is a set of examples or observations paired with target values, categories, or other task-relevant annotations.
• transfer learningused by · Transfer learning is a machine-learning technique that reuses knowledge learned from one task, domain, or dataset to improve learning or performance on another.
• self-supervised learningused by · Self-supervised learning is a machine-learning approach that derives training targets or supervisory signals from the data itself rather than relying on externally supplied labels.
• autoencoderused by · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.

This explainer was generated by Manning's knowledge graph from the text of Grokking Machine Learning. The section links point to the parts of the book it draws on.