Preferred Label : Overfitting;
EFMI definition : In ML, overfitting occurs when a model learns the training data too thoroughly, capturing
not just the fundamental patterns, but also noise or random fluctuations. Such a model
might excel on the training data, but struggles to generalize to new, unseen data.
Source: Adapted from IEEE Standards. (2022). IEEE Standard for Performance and Safety
Evaluation of Artificial Intelligence Based Medical Devices: Terminology (IEEE Std
2802 ‐2022). https://standards.ieee.org/ieee/2802/7460/;
Origin ID : 300244;
See also inter- (CISMeF)
In ML, overfitting occurs when a model learns the training data too thoroughly, capturing
not just the fundamental patterns, but also noise or random fluctuations. Such a model
might excel on the training data, but struggles to generalize to new, unseen data.
Source: Adapted from IEEE Standards. (2022). IEEE Standard for Performance and Safety
Evaluation of Artificial Intelligence Based Medical Devices: Terminology (IEEE Std
2802 ‐2022). https://standards.ieee.org/ieee/2802/7460/
