A classifier is a machine learning model or algorithm that takes an input and assigns it to one of a set of predefined categories or labels. Rather than producing a number or an open-ended answer, it makes a decision: this email is spam, this transaction looks fraudulent, this customer belongs in a particular segment.
Classifiers are usually built through supervised learning, where the model studies labeled training data, examples that already carry the correct answer, and learns the patterns that separate one category from another. The same basic approach powers sentiment analysis, image labeling, medical triage tools, and content moderation systems.
Not every classifier works the same way. A binary classifier chooses between two outcomes, such as fraud or not fraud. A multiclass classifier picks one label from several options, like sorting support tickets by department. A multilabel classifier can assign more than one tag to a single input, such as marking an image as both outdoor and nighttime.
For a business leader, accuracy alone does not tell the full story. What matters is the cost of getting it wrong: false positives that block legitimate customers, false negatives that let fraud slip through, and the confidence threshold that decides how sure the model must be before it acts. Ongoing monitoring matters too, because customer behavior and data patterns shift over time.
If classifiers make you want the real foundations behind model training and evaluation, Coursera’s Machine Learning Specialization is a strong next step. It covers core machine learning concepts, including supervised learning, classification, model evaluation, and how to think clearly about errors.*