Träningsförlust

Träningsförlust, often translated as "training loss" or "training error," refers to the error a machine learning model makes on the data it was trained on. This metric is crucial for understanding how well a model has learned the underlying patterns in the training dataset.

A low training loss indicates that the model is fitting the training data well, meaning it can accurately predict the target values for the examples it has already seen. Conversely, a high training loss suggests that the model has not learned the training data effectively. This could be due to various reasons, such as an insufficient model capacity, too few training epochs, or issues with the data itself.

While a low training loss is desirable, it's not the sole indicator of a good model. A model that achieves very low training loss but performs poorly on unseen data is said to be overfitting. Overfitting occurs when a model learns the training data too precisely, including its noise and specific idiosyncrasies, hindering its ability to generalize to new examples. Therefore, training loss is typically analyzed in conjunction with validation loss and test loss to assess the overall performance and generalization capability of a machine learning model.