Training Loss

Training loss is a measure used in machine learning to quantify the error between the predicted outputs of a model and the actual outcomes during the training phase. It helps in adjusting the model's weights through optimization algorithms like gradient descent.

Understanding Training Loss

In machine learning, models learn by minimizing this loss. Training loss is calculated using a loss function, chosen based on the specific task, such as mean squared error for regression tasks. The decrease in training loss over epochs indicates learning, whereas if the loss stops decreasing, it may imply that the model has learned as much as it can from the training data.