Classification

How it is used

Classification can be binary (two classes), multi-class (one of many) or multi-label (several at once). The model is called a classifier, and its quality is measured with accuracy, precision, recall and F1 score.

Example sentences

1. This is a multi-label classification problem: one ticket can be about billing and delivery at the same time.

2. For simple classification tasks, a small fine-tuned model is cheaper than an LLM.

Common collocations

• binary classification
• multi-class classification
• image classification
• text classification
• classification model

Related terms

• regressionA task in which a model predicts a number on a continuous scale, such as a house price, a delivery time or tomorrow's temperature.
• supervised learningA type of machine learning in which the model learns from examples that come with correct answers (labels), so that it can predict the answer for new examples.
• labelThe correct answer attached to a training example, such as 'spam' for an email or 'cat' for a photo, which a supervised model learns to predict.
• accuracyThe share of a model's predictions that are correct, out of all its predictions. A model with 90% accuracy is right on nine of every ten examples.
• confusion matrixA table that shows, for each true class, how many examples the model assigned to each class, so you can see at a glance which classes it mixes up.

This word in other fields

The same English word can have a different meaning in another field:

• classification · Software Engineering
• classification · Data Science & Analytics

More terms: Machine Learning Basics

• artificial intelligence
• machine learning
• model
• unsupervised learning
• reinforcement learning
• clustering
• state-of-the-art