What Does Labelled Data Look Like?
Labelled data forms the backbone of supervised machine learning. This article explains how labelled data appears in real projects and shows practical examples across several data types.
What Is Labelled Data?
Labelled data consists of inputs paired with correct outputs. Each example contains raw information—such as text, images, or numbers—alongside a label that identifies its category, value, or class. Systems learn from these pairs to find patterns and apply them to new, unseen data.
In practice, labelled data appears as structured records. A dataset may be stored in tables, JSON files, or spreadsheets. Rows usually represent individual examples, while columns hold features and labels. In a text classification task, one column might store sentences, while another contains labels like “positive” or “negative.”
Common Formats of Labelled Data
Labelled data takes different forms depending on the problem being solved.
Tabular Data
Spreadsheets or CSV files are common when working with numerical or categorical values. Consider a dataset used to predict house prices:
| Size (sq ft) | Bedrooms | Location | Price (\$) |
|---|---|---|---|
| 1500 | 3 | Urban | 300000 |
| 2000 | 4 | Suburban | 450000 |
| 1200 | 2 | Rural | 200000 |
Here, Size, Bedrooms, and Location are features. Price is the label, represented as a continuous value for a regression task.
Text Data
For sentiment analysis, datasets pair sentences with emotional categories:
| Text | Sentiment |
|---|---|
| “Great movie, loved it!” | Positive |
| “Boring plot, waste of time.” | Negative |
| “Okay film, nothing special.” | Neutral |
In this case, the labels are categorical. The system learns how wording, tone, and phrasing relate to each sentiment.
Image Data
Image datasets consist of files paired with annotation records. A simple image classifier might store:
| Image Path | Label |
|---|---|
| cat_001.jpg | Cat |
| dog_002.jpg | Dog |
| cat_003.jpg | Cat |
For object detection tasks, labels often include bounding boxes. These define coordinates around items in the image and attach class names such as “cat,” “car,” or “person.”
Audio Data
Audio datasets usually match sound clips with transcripts or categories. For example, in emotion recognition:
| Audio File | Transcript | Emotion |
|---|---|---|
| speech_01.wav | “I’m so happy today!” | Happy |
| speech_02.wav | “This is frustrating.” | Angry |
Here, the waveform is the input, while the transcript and emotion tags act as labels.
Real-World Examples Across Domains
Healthcare
Medical images and patient records often include diagnostic labels such as “benign,” “malignant,” or specific disease names.
| X-Ray ID | Image File | Diagnosis |
|---|---|---|
| XR001 | lung_001.png | Pneumonia |
These labels support systems that assist clinicians with screening and review.
Finance
Financial datasets may attach decision or risk labels to time-series data.
| Date | Open | High | Low | Close | Action |
|---|---|---|---|---|---|
| 2025-01-01 | 100 | 105 | 98 | 102 | Buy |
Such labels help models learn patterns tied to trading signals or risk categories.
Natural Language Processing
Question–answer datasets pair user queries with expected responses.
| Question | Answer |
|---|---|
| Capital of France? | Paris |
| Largest planet? | Jupiter |
These examples guide systems that respond to queries or retrieve information.
How Labelled Data Gets Created
Labels are commonly produced through human annotation, expert review, or existing records. People may read text and assign categories, draw shapes around objects in images, or verify transcripts of speech. Semi-automated tools often speed up this process, while quality checks help reduce mistakes. Some workflows use active learning, where uncertain samples are prioritised for labelling to make better use of time and resources.
Why Labelled Data Matters
Labelled data trains models to connect inputs with meaningful outputs. Weak or inconsistent labels limit performance, while diverse and accurate labels lead to more reliable results. As tasks grow more complex, datasets often scale to thousands or millions of labelled examples.
In practical terms, labelled data looks like organised collections of paired inputs and outputs. Whether stored as tables, annotated files, or structured records, these datasets provide the guidance systems need to learn from real examples and apply that learning to new situations.
