What does "Training Error" mean?

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Training error refers to the difference between the predicted values from a model and the actual values in the training data. When a model learns from data, it tries to make predictions based on the patterns it sees. If the model's predictions are not accurate when compared to the true outcomes, it has a training error.

A low training error means that the model is doing a good job at learning from the training data. However, if the training error is too low, it may indicate that the model is too finely tuned to the training data and might not perform well on new, unseen data. This is often called "overfitting."

On the other hand, if the training error is high, it suggests that the model is struggling to learn the underlying patterns within the training data. This situation can arise if the model is too simple or if the training data is not sufficient.

Overall, training error is an important measure to evaluate the performance of a model during its learning process. Balancing training error with performance on new data is key to building effective machine learning models.