What Is a Label in Machine Learning?
A label is a tag that provides the correct answer or classification for a piece of data, used to teach an AI system. An AI system, by itself, doesn't understand the context or meaning of raw data like images, text, or financial transactions. It cannot learn to perform a specific task, like identifying fraudulent transactions, without first being shown what a fraudulent transaction looks like compared to a legitimate one.
How it helps#
By providing data with correct labels, you create a "study guide" for the AI. The system studies thousands of labeled examples to learn the patterns that connect the data to its correct outcome, enabling it to make accurate classifications or predictions on new, unseen data.
How it works#
The process begins with collecting raw data relevant to a business task. Human annotators then go through this data and apply a pre-defined label to each piece. For example, in a system designed to sort customer feedback, a person would read each submission and label it as "Positive," "Negative," or "Neutral."
This collection of data and its corresponding labels is used as a training set. The AI model processes this set, repeatedly attempting to guess the label based on the data. After each guess, it compares its answer to the correct label and adjusts its internal logic to improve its accuracy for the next attempt. This process is repeated until the model becomes highly proficient at the task.
How it is different#
A label provides an explicit, correct answer for the AI to learn from. This is different from training an AI with unlabeled data, where the system is given a large amount of information and tasked with finding its own patterns and groupings without any predefined answers. For instance, instead of telling an AI which customers are 'high-value,' you would give it all customer data and let it discover different customer segments on its own.