Träningsfelet

Träningsfelet, often translated as "training error" or "empirical error," refers to the difference between the predicted output of a machine learning model and the actual target values on the training data. It is a fundamental metric used to evaluate how well a model has learned the patterns present in the dataset it was trained on.

In simpler terms, it's a measure of how many mistakes a model makes when it's tested on the very same data it used to learn. A low training error indicates that the model has successfully captured the relationships within the training set. Conversely, a high training error suggests that the model has not learned effectively or that the training process was insufficient.

However, a very low training error alone is not always a sign of a good model. If a model achieves an extremely low or zero training error, it might be "overfitting" the training data. Overfitting occurs when a model learns the training data too well, including its noise and specific idiosyncrasies, to the detriment of its ability to generalize to new, unseen data. Therefore, while training error is important, it must be considered alongside other metrics, such as validation error or test error, to get a comprehensive understanding of a model's performance.