Classification
Quick Answer
Classification is a supervised learning task where the model assigns inputs to one of a predefined set of categories. Binary classification distinguishes between two classes; multi-class and multi-label variants handle more complex labelling problems.
In Depth
What Classification really means
Classification underlies spam filtering, fraud detection, medical diagnosis support, content moderation, support ticket routing and many other workaday AI systems. Key metrics include accuracy, precision, recall, F1 and — crucially — fairness across groups.
Imbalanced datasets (e.g. 99% non-fraud, 1% fraud) require special techniques such as resampling, class weighting and careful metric choice. Blindly optimising accuracy on imbalanced data produces models that look good but are useless.
Why It Matters
Business relevance for UK organisations
Classification is the most commercially important supervised learning task. UK organisations use it everywhere from credit approval to customer segmentation and operational triage.
Real-world example
How this shows up in practice
A London neobank trained a classification model to flag high-risk onboarding applications, improving fraud detection by 31% without increasing false positives.
Related Terms
Continue exploring
Supervised Learning
Supervised learning is a machine learning approach in which the model is trained on a dataset containing inputs paired with their correct outputs (labels). The model learns to map inputs to outputs, enabling it to predict labels for new, unseen examples.
AdvancedRegression
Regression is a supervised learning task where the model predicts a continuous numeric value rather than a category. Typical applications include forecasting demand, estimating prices, predicting customer lifetime value and modelling equipment wear.
BasicsMachine Learning
Machine Learning (ML) is a subfield of AI in which systems learn patterns from historical data rather than following explicitly programmed rules, enabling them to make predictions or decisions on new, unseen data as conditions evolve.
BasicsTraining Data
Training data is the dataset used to teach a machine learning model the patterns it needs to perform its task. The quality, quantity, diversity and recency of training data directly determine how accurate and fair the resulting model will be.