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Glossary

Decision tree

What is Decision tree?

A decision tree is a model that represents a decision as a sequence of simple yes/no questions: each branch tests one feature and each leaf gives an outcome. In machine learning, such trees are learnt automatically from data and used for classification or forecasting. Their main strength is transparency – for instance, showing why a loan application was flagged based on income, term and payment history.

How KI.ordo helps

In KI.ordo flows, simple decision paths can be modelled with the ‘Condition’ (yes/no) and ‘AI decider’ (up to 8 categories) building blocks.

Learn more →: Decision tree

Topics

  • Machine learning
  • Classification
  • Transparency

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Related terms

Random forest regression

Random forest regression predicts numerical values by combining many decision trees. Each tree is trained on a random sample of the data and a random selection of features; the result is the average of all the trees' predictions. This makes the method more robust and less prone to overfitting than a single tree. It is used, for example, for sales forecasts, price estimates or energy demand – often as a solid starting point for tabular data.

  • regression
  • ensemble method
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Gradient boosting

Gradient boosting is a machine learning technique that combines many simple models – usually small decision trees – one after another. Each new tree is trained specifically to reduce the remaining errors of the previous ones. The result is highly accurate models for tabular data, for example for credit risk, sales forecasts or churn probability. Well-known implementations are XGBoost, LightGBM and CatBoost; careful tuning is needed to avoid overfitting.

  • Machine learning
  • Ensemble method
  • +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

Heuristics

Heuristics are rules of thumb or simplified strategies that lead to a good, though not guaranteed optimal, solution within reasonable time. In computer science and AI they help narrow down huge search spaces – for example in route planning, where the straight-line distance to the destination serves as an estimate, or in chess, where positions are evaluated. People also use heuristics in everyday life and business decisions, with the risk of systematic thinking errors.

  • Problem solving
  • Algorithms
  • +1