Patience

The term "patience" in the context of machine learning typically refers to the number of epochs to wait before terminating training if no improvement in the validation metric is observed. It is used in conjunction with techniques such as early stopping to prevent overfitting and improve the generalization of the model.

The value of "patience" is a hyperparameter that specifies the maximum number of epochs to continue training without improvement in the validation metric. The min and max values provided (0 and 100000, respectively) indicate the range within which the "patience" hyperparameter can be specified.

Here's what it means:

• A "patience" value of 0 means that the training will stop immediately if no improvement is observed after the first epoch.

• A "patience" value of 100000 means that the training will continue for a maximum of 100000 epochs even if no improvement is observed.

In practice, the value of "patience" is typically chosen based on experimentation and validation performance. It should strike a balance between allowing sufficient time for the model to learn meaningful patterns in the data and avoiding overfitting by stopping training when performance on the validation set starts to degrade.

A common approach is to start with a relatively small value for "patience" and gradually increase it if necessary. Values between 5 and 20 are often used as a starting point, but the optimal value may vary depending on the dataset, model architecture, and training objectives.