Mean absolute error (MAE) shows how far a model’s predictions deviate from the actual values on average, regardless of direction. It is expressed in the unit of the target variable, which makes it easy to understand. Example: an MAE of 12 for a daily sales forecast means the model is off by 12 units on average. It gives less weight to outliers than squared error measures do.
Linear regression is a statistical method that describes the relationship between a target variable and one or more predictors using a straight line or plane. The coefficients are usually chosen to minimise the sum of squared deviations. The method is easy to understand and interpret: for example, it can estimate how much revenue rises when the advertising budget increases by €1,000. It reaches its limits with non-linear relationships.
The generalisation error measures how far off an AI model is on new, previously unseen data – as opposed to its error on the training data. A large gap between the two points to overfitting: the model has memorised examples instead of learning general patterns. It is estimated using held-out test data or cross-validation. It is the figure that really counts, because in practice a model almost only meets new cases.
Cross-validation is a method for checking how well a model generalises to new, unseen data. In k-fold cross-validation, the data is split into k parts; the model is trained k times on k−1 parts and tested on the remaining part each time, after which the results are averaged. This helps detect overfitting and compare models or settings fairly, especially when little data is available.
Predictive modelling is the technical core of predictive analytics: example data with known outcomes is used to build a model that predicts a value or class for new cases. Common methods include regression, decision trees, random forests and neural networks. A model might, for instance, estimate a property price from floor area, location and year of construction. Before deployment it is tested on held-back data to detect overfitting.
