Zusammenfassung
Während Machine Learning (ML) in vielen Domänen sehr gut funktioniert, wie die Leistung selbstfahrender Autos zeigt, bergen vollautomatisierte ML-Methoden in komplexen Domänen die Gefahr der Modellierung von Artefakten. Ein Beispiel für eine komplexe Domäne ist die Biomedizin, wo wir mit hochdimensionalen, probabilistischen und unvollständigen Datenmengen konfrontiert sind. In solchen Problemstellungen kann es vorteilhaft sein, nicht auf menschliches Domänenwissen zu verzichten, sondern vielmehr menschliche Intelligenz und ML zu kombinieren.
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Holzinger, A. Interactive Machine Learning (iML). Informatik Spektrum 39, 64–68 (2016). https://doi.org/10.1007/s00287-015-0941-6
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DOI: https://doi.org/10.1007/s00287-015-0941-6