Which process involves using a trained model to make predictions on new, unseen data?

The correct concept here is the process of inference, which refers to the phase where a trained machine learning model is applied to new, unseen data to generate predictions. During this phase, the model uses the patterns and learned representations acquired during the training process to make informed guesses about the outcomes for the new inputs it has not encountered before.

Training is distinct from inference, as it is the initial phase where the model learns from the training dataset by adjusting its parameters based on the data features and the provided labels. Iterations involve repeatedly refining the model during the training phase, often through methods like gradient descent. Validation, on the other hand, is a process that assesses the model’s performance on a separate validation dataset to ensure it generalizes well beyond just the training data. It is crucial for tuning the model but does not directly involve making predictions on new data.

Thus, inference is the correct answer, as it explicitly describes the process of applying a trained model to gain insights from previously unseen datasets.