Unlabeled data
Unlabeled data consists of examples that lack target labels or task-specific annotations.
Grokking Machine Learning
Data whose observations do not have associated target labels or outcome annotations.
- How it is used
- Supports unsupervised learning by providing observations from which algorithms extract patterns without target labels.
- 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.
Read how Grokking Machine Learning explains unlabeled data →
Outlier Detection in Python
Data whose instances do not have assigned target labels or annotations.
- How it is used
- Supports unsupervised outlier detection and complements supervised models when labeled and unlabeled records coexist.
- How deep it goes
- Explains distributional value, manual labeling, and classifier limitations without developing specific detection algorithms.
- Examples
- Uses detector-flagged records, near-threshold records, and a 100,000-row dataset with approximately 300 manually labeled examples.
- What you can do after
- Evaluate whether unlabeled records can support outlier detection or semisupervised modeling while recognizing labeling and assumption limits.
Read how Outlier Detection in Python explains unlabeled data →
Natural Language Processing in Action, Second Edition
Unlabeled text is textual data that lacks human-provided category, entity, or other annotation labels.
- How it is used
- Supports unsupervised NLP systems that learn linguistic relationships and world knowledge from large text collections without manual labels.
- How deep it goes
- Explains the label-free learning principle through Word2Vec and word contexts, but stops short of detailing corpus preparation or training procedures.
- Examples
- Uses corpora mentioning Marie Curie, the Timbers, Portland, science, soccer, and cities to illustrate contextual learning.
- What you can do after
- Evaluate/understand how NLP models can extract relationships and knowledge from text without manually assigned target labels.
Read how Natural Language Processing in Action, Second Edition explains unlabeled data →
Building LLM Applications with DSPy
Data examples that lack target outputs or labels.
- How it is used
- Supports evaluating and optimizing summarization when examples lack reference summaries.
- How deep it goes
- Defines input-only examples and their use for evaluation and optimization, without detailing the optimization procedure.
- Examples
- Uses original documents without ground-truth summaries as summarization examples.
- What you can do after
- Apply it via DSPy to evaluate and optimize examples without ground-truth summaries.
Read how Building LLM Applications with DSPy explains 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.
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