Glossary

Discriminative models

What is Discriminative models?

Discriminative models learn to tell data points apart by directly determining the boundary between classes – they answer the question 'Which category does this input belong to?'. Examples include logistic regression, support vector machines and many neural classifiers, for instance for spam detection. Generative models, by contrast, capture how the data itself arises and can therefore produce new examples.

Topics

  • classification
  • machine learning
  • model types

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Related terms

Generative AI

Generative AI refers to AI systems that create new content – text, images, audio, video or program code – rather than merely sorting or assessing existing data. They are based on models that learn patterns from large amounts of data and use them to produce plausible new outputs. In business it is used, for example, to draft emails, summaries, product descriptions or image ideas. Results can contain errors and should be checked before use.

  • Core concept
  • Content creation
  • +1

Logistic regression

Logistic regression is a statistical method that predicts the probability of one of two outcomes, such as “buys” or “does not buy”. It weights the input features and converts their sum into a value between 0 and 1 using an S-shaped function. Because the weights are easy to interpret, it is widely used in credit scoring, medicine and marketing, often as a solid baseline for more complex models.

  • statistics
  • classification
  • +1

Classifier

A classifier is a model or method that assigns inputs to one of several predefined categories. Classic variants such as decision trees or naive Bayes are trained on labelled examples; language models can also sort texts based on a description of the categories. Typical uses include spam detection, sorting customer enquiries by topic and categorising receipts. Quality is measured with metrics such as accuracy, precision and recall.

  • Classification
  • Machine learning
  • +1
Template

Naive Bayes

Naive Bayes is a family of simple classification methods based on Bayes’ theorem. They are called “naive” because they assume all features are independent of one another, which is rarely true in practice but often still produces good results. The methods are fast, need little training data and are particularly well suited to text. A classic example is the spam filter that sorts emails based on typical words.

  • classification
  • statistics
  • +1