model stability
Model stability, also known as algorithmic stability, means that a model's predictions remain consistent and accurate even when facing slight variations in input data.
Model stability in AI implies that a model delivers reliable and trustworthy outputs despite minor variations in input data, preventing unexpected or erratic behavior and promoting confidence in its decision-making process.
Stability can be influenced by various factors, including the model’s complexity, the choice of input representations, the learning algorithm, and the training strategy. For example, simpler models like linear models are typically more stable in creation, while complex models like deep learning models may exhibit less stability[2][3]. Learning strategies such as ensemble models and incremental training can also affect stability[3].
Citations:
[1] https://en.wikipedia.org/wiki/Stability_%28learning_theory%29
[2] https://www.linkedin.com/pulse/stability-machine-learning-model-damjan-krstajic
[3] https://machinelearning.apple.com/research/model-stability-with-continuous-data
[5] http://www.offconvex.org/2016/03/14/stability/