Evaluation metrics while training the model.

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Evaluation metrics are used to assess the performance of a model during training.

Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.
Accuracy measures the proportion of correctly classified instances out of the total instances.
Precision measures the proportion of true positive predictions out of all positive predictions.
Recall measures the proportion of true positive predictions out of all actual positive instances.
F1 score is the harmonic mean of precision and recall, providing a balance between the two.
ROC-AUC is the area under the receiver operating characteristic curve, indicating the model's ability to distinguish between classes.

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