Description: Validation loss is a fundamental concept in the training of machine learning models, especially in the context of deep learning. It refers to the loss value that a model presents when evaluated on a validation dataset, which is a subset of data not used during training. This value is crucial for understanding how the model is performing on unseen data, helping to identify overfitting issues. A model that has low loss on the training set but high loss on the validation set may be learning specific patterns from the training data rather than generalizing well to new data. Validation loss is calculated using a loss function, which measures the discrepancy between the model’s predictions and the actual labels. This metric can be monitored during the training process to adjust hyperparameters and improve model performance. Therefore, validation loss is a key indicator of a model’s ability to generalize and is essential for the evaluation and optimization of models in supervised learning tasks.
