Labeled data
Labeled data is a set of examples or observations paired with target values, categories, or other task-relevant annotations.
Outlier Detection in Python
Labeled data consists of data items accompanied by target labels or annotations used for supervised learning.
- How it is used
- Turns reviewed outlier records into evidence for categorization, later classification, improved scoring, and regression tests.
- How deep it goes
- Explains binary and specific labels, how they accumulate, and their use in testing and learning; does not specify a labeling protocol.
- Examples
- Uses pre-failure machinery logs, financial-outlier categories, and lists of known real outliers as concrete cases.
- What you can do after
- Operate/configure it by retaining reviewed labels, using them when training is feasible, and checking retrained detectors against known cases.
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Natural Language Processing in Action, Second Edition
A labeled dataset is a dataset whose examples are paired with target labels or annotations for supervised learning.
- How it is used
- Supports supervised NLP systems by pairing language examples with desired responses, scores, or intents.
- How deep it goes
- Defines labeled examples and shows their training and maintenance roles, but does not detail annotation standards or quality-control procedures.
- Examples
- Uses chatbot responses, sentiment-scored reviews, hashtags, and intent-labeled utterances as concrete dataset examples.
- What you can do after
- Build NLP models by supplying labeled examples, including sentiment classifiers and chatbot understanding systems.
Read how Natural Language Processing in Action, Second Edition explains labeled data →
Grokking Machine Learning
Data containing a designated target feature or label that a model is intended to predict.
- How it is used
- Supports supervised learning by supplying target values or categories for prediction.
- How deep it goes
- Defines labels, distinguishes numerical and categorical targets, and shows prediction examples without detailing specific algorithms.
- Examples
- Uses pet images, dog-or-cat tags, animal weights, spam-or-ham emails, and health states as concrete labeled datasets.
- What you can do after
- Evaluate whether a dataset contains a target feature and identify whether its labels are numerical or categorical.
The Art of AI Product Development
Labeled data is data paired with target annotations, classifications, or outcome values for supervised learning or evaluation.
- How it is used
- Provides target results for supervised classification and fine-tuning, with labels chosen to reflect the intended user task.
- How deep it goes
- Defines input-label pairs and details ways to collect and check them, without specifying annotation-tool procedures or training code.
- Examples
- Uses labeled user segments, star-rated reviews, and client documents paired with topic categories.
- What you can do after
- Build it from scratch: pair inputs with target labels, address category imbalance, and sample records to check consistency.
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Investing for Programmers
Labeled data is data paired with known target values or categories for supervised learning.
- How it is used
- Uses known financial outcomes to train models that classify stocks or estimate future price changes.
- How deep it goes
- Explains labels, features, dataset structure, and model training, while deferring detailed feature-selection challenges.
- Examples
- Uses stock directions, returns, company clusters, next-day prices, and a random-forest prediction table.
- What you can do after
- Build it from scratch by assembling features and targets, training a supervised model, and comparing predictions with held-out outcomes.
Inside AI
Labeled data is a dataset whose examples are paired with target labels or outcomes.
- How it is used
- Supplies input-target pairs for supervised learning, including deep-learning image classification.
- How deep it goes
- Defines labels through input-target association and illustrates their use, without detailing how examples are annotated.
- Examples
- Uses cat-and-dog photographs and ImageNet’s human-annotated images for object recognition testing.
- What you can do after
- Evaluate/understand whether a deep-learning project has enough labeled data and whether its training examples suit the intended application.
Related concepts
- supervised learningused by · Supervised learning is a machine-learning approach that learns to map inputs to target outputs from examples paired with known labels or values.
- datakind of · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.
- fine-tuningused by · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
- predictive analyticsused by · Predictive analytics is the use of data and statistical or computational methods to estimate future or otherwise unknown outcomes.
- reward modeluses · A reward model is a learned model that assigns scores to outputs, actions, or outcomes to represent their desirability according to preferences or evaluation criteria.
- unlabeled datacontrasts with · Unlabeled data consists of examples that lack target labels or task-specific annotations.
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