What is labels?
Labels are the names or categories we assign to data so a computer knows what each piece of information represents. In machine learning, a label tells the algorithm the correct answer for a given example, such as “cat” or “dog” for an image, or “spam” or “not spam” for an email.
Let's break it down
- Data point: The raw information (e.g., a picture, a sentence, a sensor reading).
- Feature: The measurable parts of the data point that the model looks at (e.g., pixel colors, word frequencies).
- Label: The correct answer that matches the data point (e.g., “cat”, “positive review”). During training, the model sees many pairs of features and labels and learns to predict the label when it only gets the features later.
Why does it matter?
Without labels, a computer can’t learn the difference between categories-it would just see random numbers. Labels give the model a target to aim for, allowing it to recognize patterns, make predictions, and automate decisions that would otherwise require human judgment.
Where is it used?
- Image classification (identifying objects in photos)
- Email filtering (spam vs. not‑spam)
- Sentiment analysis (positive, neutral, negative reviews)
- Medical diagnosis (disease vs. healthy)
- Voice assistants (recognizing spoken commands)
Good things about it
- Enables supervised learning, which often yields high accuracy.
- Makes model performance easy to measure (compare predicted labels to true labels).
- Helps create reliable, repeatable systems for many real‑world tasks.
- Labels can be shared across projects, building larger, more useful datasets.
Not-so-good things
- Collecting accurate labels can be time‑consuming and expensive.
- Human bias in labeling can lead to biased models.
- Mistakes in labels (noisy data) can confuse the algorithm and reduce performance.
- Over‑reliance on labels may limit the model’s ability to discover new patterns not previously defined.