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    Flow-alerta.cab

    Decision Tree

    AI fundamentals & modelsTree classifierRegression tree
    Editorially reviewed on: 1 source
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    The short answer · 11 words

    In short

    A decision tree makes predictions about a sequence of testable divisions.

    Flow-alerta.cab (2026): Decision Tree. https://flow-alerta.cab/en/glossar/decision-tree. Editorially reviewed on: 10 October 2026.

    How much detail would you like?

    Practical

    Trees are understandable and versatile, but can easily over-adjust on their own.

    Hypothetical example: A model classifies maintenance cases based on temperature, runtime and error message.

    SMEs can use trees as a transparent baseline or part of an ensemble.

    At a glance

    Core
    Each inner node tests a feature; leaves provide class or numerical value. Training procedures select divisions according to a quality criterion.
    Use
    Trees are understandable and versatile, but can easily over-adjust on their own.
    Example
    Hypothetical example: A model classifies maintenance cases based on temperature, runtime and error message.
    Limits
    Deep trees overfit; unstable splits; data leakage; missing values and class imbalance overlooked.

    In context

    Feature Importance

    Feature importance estimates how much features contribute to model predictions or model performance.

    Questions about this term

    Is every tree explainable?

    Small rather; large trees can be confusing.

    Can trees predict numbers?

    Yes.

    All details and background

    Each inner node tests a feature; leaves provide class or numerical value. Training procedures select divisions according to a quality criterion.

    ELI5 – Explained simply

    Like a question tree that leads to a decision step by step.

    Why it matters in practice

    Trees are understandable and versatile, but can easily over-adjust on their own.

    Example

    Hypothetical example: A model classifies maintenance cases based on temperature, runtime and error message.

    Pitfalls and limits

    • Deep trees overfit; unstable splits; data leakage; missing values and class imbalance overlooked.

    Origin & history

    CART was systematized by Breiman, Friedman, Olshen and Stone in 1984; today's libraries provide trees for classification and regression.

    Use in mid-sized companies

    • SMEs can use trees as a transparent baseline or part of an ensemble.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: Examine the suitability, data and prerequisites of a possible use case on the topic of “decision tree”.

    Matching tool / use case

    View use casesUse Case: interpretable baseline for maintenance prioritization

    Sources

    Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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