Multi-Class Classification

Multi-class classification is a machine learning task where each item is assigned exactly one label from three or more possible classes. The classes are mutually exclusive: a page is a blog post or a product page or an FAQ, never two at once. It sits between binary classification (two options) and multi-label classification (several labels per item) in MLforSEO’s breakdown of classification types.

How it works

It is usually a supervised task: you label examples, train a model, and the model predicts a class for new items.

• Many models handle multiple classes directly, including decision trees, random forests, Naive Bayes and neural networks. Neural networks typically end with a softmax layer that gives a probability for each class, adding up to 1.
• Models that are binary at heart, such as logistic regression and SVMs, use a strategy. One-vs-rest trains one yes/no model per class and picks the most confident; one-vs-one trains a model for every pair of classes and takes a vote. Libraries like scikit-learn usually do this for you.
• Today you can also prompt an LLM with the class definitions, which works well for small batches but needs checking for consistency.

To evaluate it, look at a confusion matrix (which classes get mixed up), and at precision, recall and F1 per class, averaged across classes.

How it’s used in SEO and marketing

• Page type identification across a site crawl (blog, product, category, landing page, case study).
• Search intent labelling with one dominant intent per keyword.
• Routing support tickets or leads to the right team.
• Assigning each keyword to one funnel stage.

Example

MLforSEO’s classification post uses the question “what type of page is this: blog, product, FAQ, landing page or case study?” You label a few hundred URLs from a crawl, use the page title, URL path, word count and presence of price or schema markup as features, and train a model. The confusion matrix might show that landing pages and case studies get confused most often, which tells you where to add features or clearer definitions.

Limitations and things to watch

• Real content often fits more than one class; if so, multi-label classification is the better framing.
• Rare classes get ignored when the data is imbalanced; collect more examples or weight them.
• Vague or overlapping class definitions produce inconsistent labels and a weak model.
• Add an “other” class so unusual items are not forced into the wrong bucket.

Learn it on MLforSEO

• What Machine Learning Classification Is, and Where It Fits in SEO
• Complete Guide Understanding ML Classification for Marketers
• Search-Intent Classification with Machine Learning: The Approaches Explained
• Introduction to Machine Learning for SEO course