A model can be predictive, descriptive, or adaptive.

Predictive models discover patterns in historical data and extract fundamental trends and relationships between factors (or features). They are used to predict and classify future events or observations. Predictive analytics is used in a variety of fields, including marketing, insurance, and pharmaceuticals. Predictive models are created through supervised learning using a preselected training set.

Descriptive models attempt to find unusual patterns or affinities in data by grouping observations into clusters with similar properties. These models define the first and important step in knowledge discovery. They are generated through unsupervised learning.

A third category of models, known as adaptive modeling, is created through reinforcement learning. Reinforcement learning consists of one or several decision-making agents that recommend and possibly execute actions in the attempt of solving a problem, optimizing an objective function, or resolving constraints.