What measures the accuracy of a model during its training phase?

The loss function is a critical component in evaluating the performance of a model during the training phase. It quantifies how well the model's predictions align with the actual target values. This mathematical function takes the predicted outputs of the model and compares them to the true labels, providing a score that indicates the extent of error. A lower value of the loss function indicates better model performance, as it suggests that the predictions are closer to the true values.

During training, the model optimizes its parameters by minimizing the loss function through various techniques, such as gradient descent. This iterative adjustment allows the model to learn patterns from the training data. While accuracy score, precision rate, and recall score are important metrics for assessing model performance, they typically apply to evaluation on validation or test datasets rather than during training itself. The loss function serves as the foundation for guiding how the model learns, making it essential during the training phase.