Identify the model which is trained with data in only a single batch.

The model which is trained with data in only a single batch is known as a Batch Gradient Descent model.

Here are the steps to identify it:

1. Check the Training Process: In Batch Gradient Descent, the model is trained using the entire dataset at once. This means the model makes only one update for each iteration of the training algorithm. If the model's training process involves updating the parameters using the entire dataset at once, it is a Batch Gradient Descent model.

2. Look at the Computation Time: Batch Gradient Descent models often take a long time to compute, especially with large datasets. This is because they use all the data at once during training.

3. Check for Consistent Error Reduction: Batch Gradient Descent models often show a consistent reduction in error with each iteration. This is because they use the entire dataset to calculate the gradient of the cost function, leading to a more accurate direction towards the minimum.

4. Look at the Memory Usage: Batch Gradient Descent models often require a lot of memory, as they need to store the entire dataset in memory at once.

5. Check the Convergence: Batch Gradient Descent models often converge to a global minimum of the cost function. This is because they use the entire dataset to calculate the gradient, which gives a more accurate direction towards the minimum.