What does the term Nondeterminism refer to in machine learning?

Nondeterminism in machine learning refers to the presence of randomness or the influence of external factors that can affect the processes and outcomes of machine learning models. This concept suggests that given the same input, a model may produce different outputs due to underlying uncertainties or variations in the data and algorithms used.

In the context of machine learning, nondeterminism can arise from various sources, including random initialization of model parameters, stochastic algorithms employed during training, and variability in the data itself. This inherent unpredictability means that outcomes can diverge even under seemingly identical conditions.

Consequently, recognizing nondeterminism is essential for understanding model behavior, evaluating its performance across different runs, and enhancing its robustness against external influences. The concept stands in contrast to fully predictable outcomes, which are characteristic of deterministic processes.