Why do labels matter in machine learning?#
By providing data with correct labels, you create a "study guide" for the AI. The system studies thousands of labelled 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 are labels used to train a model?#
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 is labelled data different from unlabelled data?#
A label provides an explicit, correct answer for the AI to learn from. This is different from training an AI with unlabelled 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.