Regression
Quick Answer
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.
In Depth
What Regression really means
Linear and tree-based regression remain the workhorses for most tabular business problems, while neural networks dominate when inputs include text, images or very high-dimensional features. Metrics include mean absolute error, mean squared error and R².
A common pitfall is focusing on average accuracy while ignoring errors that matter disproportionately — for example, underestimating demand during a peak week. Choosing a business-aligned loss function often matters more than choosing an algorithm.
Why It Matters
Business relevance for UK organisations
UK retailers, energy suppliers and logistics providers rely on regression for forecasting; fintechs use it for risk and pricing; manufacturers use it for predictive maintenance.
Real-world example
How this shows up in practice
A Leeds grocery chain replaced spreadsheet forecasts with a regression model and reduced weekly waste by £62,000 across 140 stores.
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.
AdvancedClassification
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.
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.