What type of dataset is used during the training process to fine-tune the model?

During the training process, the correct type of dataset used to fine-tune the model is the training dataset. This dataset is specifically designed to train the machine learning model by providing it with labeled examples from which it can learn patterns, make predictions, and improve its accuracy.

The training dataset contains input-output pairs, which allow the model to understand the relationship between the input features and the target labels. The model iteratively adjusts its parameters based on the input data and the outcome, trying to minimize the error in its predictions. This training phase is essential for the model to capture the underlying patterns in the data that it will later apply to unseen examples.

On the other hand, the test dataset is used to evaluate the performance of the model after training; the validation dataset is typically used during training to tune hyperparameters and to prevent overfitting. The sample dataset does not specifically serve a predefined role in the training or evaluation process in the same way that the other datasets do.