What term describes the process of making predictions using a trained model?

The term that describes the process of making predictions using a trained model is inference. Inference occurs after a model has been trained and is ready to apply its learned patterns to unseen data. This involves using the model to generate predictions based on new inputs, which is a critical step in the machine learning workflow.

In the context of machine learning, training refers to the phase where the model learns from the training dataset, adjusting its parameters based on the provided examples. Testing typically involves assessing the model's performance on a separate validation or test dataset during or after training but does not specifically involve making predictions on new data inputs. Evaluation pertains to measuring the model's performance using metrics (like accuracy, precision, or recall) but also does not directly refer to the act of making predictions.

Inference, therefore, specifically captures the essence of applying a model to generate output based on new data, making it the most appropriate term in this scenario.