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Glossary

Classifier

What is 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.

How KI.ordo helps

In KI.ordo flows, the “AI router” block assigns inputs to up to eight categories, for example in the “Categorise text” template.

Learn more →: Classifier

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Topics

  • Classification
  • Machine learning
  • Categories

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Related terms

Automatic classification

Automatic classification assigns texts, documents or records to predefined categories without manual sorting. Traditionally, models learn this from labelled examples; large language models can also assign categories based on a description alone. Typical uses include sorting incoming emails by request type, assigning receipts or flagging urgent support tickets. For important cases, spot checks by people are advisable.

  • Classification
  • Automation
  • +1
Template

Text Classification

Text classification automatically assigns texts to predefined categories, for example emails by request type, reviews by sentiment or documents by topic. Previously this required training dedicated models on labelled examples; today large language models can often assign categories from a description alone. This makes it possible to pre-sort enquiries and route them to the right team faster.

  • NLP
  • Classification
  • +1
Template

Confusion Matrix

A confusion matrix is a table that compares a classifier's predictions with the actual classes. For two classes, it shows true positives, false positives, true negatives and false negatives. From this, metrics such as precision, recall and F1 score can be calculated, and it becomes clear which errors occur. Example: a spam filter that flags many genuine emails as spam has many false positives, which is often more costly than a missed spam message.

  • Model evaluation
  • Classification
  • +1

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