Machine Learning Intermediate
temperature
A hyperparameter that controls the degree of randomness of a model's output.
Plain English Explanation
A hyperparameter that controls the degree of randomness of a model's output. Higher temperatures result in more random output, while lower temperatures result in less random output. Choosing the best temperature depends on the specific application and or string values.
How is it used?
Practitioners refer to temperature when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.