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    Flow-alerta.cab

    Feature Engineering

    AI fundamentals & modelsDevelopment of characteristicsCharacteristic design
    Editorially reviewed on: 1 source
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    The short answer · 11 words

    In short

    Feature Engineering creates or transforms input features for analysis and models.

    Flow-alerta.cab (2026): Feature Engineering. https://flow-alerta.cab/en/glossar/feature-engineering. Editorially reviewed on: 10 October 2026.

    How much detail would you like?

    Practical

    Good features can improve simple models and make expertise explicit.

    Hypothetical example: Time stamps create shift, day of the week and time since last maintenance.

    SMEs can translate and document existing process knowledge into robust features.

    At a glance

    Core
    Expertise is used to clean, encode, aggregate or meaningfully combine raw data without injecting future information.
    Use
    Good features can improve simple models and make expertise explicit.
    Example
    Hypothetical example: Time stamps create shift, day of the week and time since last maintenance.
    Limits
    Leaking target or future information; learning transformations before data splitting; overlooking proxy variables and maintenance overhead.

    In context

    Feature Extraction

    Feature extraction converts raw data into representations that can use analytical techniques or models as features.

    Data Preprocessing

    Data preprocessing brings raw data reproducibly into a form that is suitable for analysis or machine learning.

    Feature Store

    A feature store manages calculated traits consistently for training and productive predictions.

    Questions about this term

    Is it superfluous in deep learning?

    No.

    When does leakage occur?

    If information is used that is missing at the time of decision.

    All details and background

    Expertise is used to clean, encode, aggregate or meaningfully combine raw data without injecting future information.

    ELI5 – Explained simply

    From raw data, suitable, understandable signals for the model are built.

    Why it matters in practice

    Good features can improve simple models and make expertise explicit.

    Example

    Hypothetical example: Time stamps create shift, day of the week and time since last maintenance.

    Pitfalls and limits

    • Leaking target or future information; learning transformations before data splitting; overlooking proxy variables and maintenance overhead.

    Origin & history

    Preprocessing tools support scaling, coding and transformation; feature development complements such steps with task-specific designs.

    Use in mid-sized companies

    • SMEs can translate and document existing process knowledge into robust features.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: Developing technically useful model features available at the time of decision.

    Matching tool / use case

    View use casesUse Case: versioned process features for forecasting models

    Sources

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

    Editorially reviewed on:

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