What does it mean for a machine learning model to be "stochastic"?

A machine learning model is described as "stochastic" when the same input can indeed produce different outputs across multiple sessions. This characteristic is often attributed to the inherent randomness in the model's processes, such as the random initialization of weights during training or the use of random sampling techniques.

In stochastic models, factors like noise in the data, the architecture of the model, or variability in the training process can lead to different predictions even when the input remains constant. This stochastic behavior is essential for certain types of models, particularly those that are designed to balance exploration and exploitation or those that rely heavily on probabilistic reasoning.

Understanding this concept is crucial because it highlights the non-linear and unpredictable nature of some machine learning systems, distinguishing them from deterministic models where the same input would yield the same output every time. Thus, the definition of stochasticity is directly connected to the variability of output given consistent input, which aligns perfectly with the correct answer.