Classifier

A classifier is a type of AI model whose job is to assign a label or category to a piece of input data. Given an email, a classifier might label it "spam" or "not spam." Given an image, it might label it "cat," "dog," or "car." Given a customer review, it might label it "positive," "negative," or "neutral."

How classifiers work

At their core, classifiers learn decision boundaries from labelled training data. During training, the model sees thousands or millions of examples that have already been categorised by humans. It learns the patterns that distinguish one category from another. When it encounters new, unseen data, it applies those learned patterns to make a prediction.

For example, a spam classifier might learn that emails containing phrases like "limited time offer," "click here now," and "unsubscribe" tend to be spam, while emails containing your colleagues' names and project-specific language tend to be legitimate.

Types of classification

• Binary classification: Two categories — yes/no, spam/not spam, fraudulent/legitimate. This is the simplest and most common type.
• Multi-class classification: Multiple categories where each input belongs to exactly one — for instance, categorising support tickets into billing, technical, account, or general.
• Multi-label classification: Each input can belong to multiple categories simultaneously — for instance, tagging a news article as both "technology" and "business."

Common classification algorithms

Many different algorithms can serve as classifiers:

• Logistic regression: Simple and fast, surprisingly effective for many text classification tasks.
• Decision trees and random forests: Good for structured, tabular data.
• Support vector machines: Effective when the number of features is high relative to the number of examples.
• Neural networks: Most powerful for complex patterns, especially in images and text, but require more data and computation.

Classification in business

Classifiers are among the most commercially valuable AI applications. They power email filtering, fraud detection, content moderation, document routing, lead scoring, medical diagnosis support, and quality control in manufacturing. In many cases, a well-built classifier delivers more business value than a flashy generative AI tool.

Evaluating classifiers

A classifier's quality is measured by metrics like accuracy, precision, recall, and the F1 score. The right metric depends on the cost of different types of errors. In medical screening, missing a positive case (low recall) is far worse than a false alarm (low precision). In email filtering, marking legitimate mail as spam (low precision) is worse than letting some spam through (low recall).