What are the internal parameters derived from a model during the training process known as?

The internal parameters derived from a model during the training process are known as model parameters. These parameters are the values that the model learns and adjusts during training to minimize the error in its predictions. In supervised learning, for instance, these parameters could represent weights in a neural network or coefficients in a regression model. Their optimization directly impacts the model's ability to accurately predict outcomes based on the input data it receives.

Model features refer to the inputs or attributes that are used to make predictions and are not the learned parameters themselves. Model variables might suggest the broader range of inputs and outputs in computations, but they don't specifically point to the internal learned parameters. Model layers pertain to the structure of certain types of models, such as neural networks, but do not refer directly to the internal values that are learned during training. Thus, model parameters is the precise term that describes the internal learned elements of a model throughout its training.