Training loss: Difference between revisions

Overfitting occurs when a model is too complex and fits the training data perfectly, yet fails to generalize well on unseen data. This leads to high training losses but low validation losses. Regularization techniques like L1 or L2 regularization can be used as penalties for complex models in order to minimize overfitting.

Underfitting occurs when a model is too simple and does not fit the training data well, leading to high training loss and validation loss. To avoid this issue, more complex models can be utilized or more data can be collected for training the model.

Training loss is like a test score for a machine learning model. The aim is to get the highest possible score by tweaking the model so it accurately predicts outcomes. Just like in school, if you get low marks on tests, you study harder in order to improve them; similarly, when a machine learning model experiences high training losses, efforts are made to reduce them and boost accuracy.

 

Imagine you have a large puzzle with many pieces. And you need to put them all together in order to complete the picture. However, sometimes you might make an error and place one piece in an incorrect location.

 

 

The training loss is like a score that shows us how well the computer is doing at putting pieces of the puzzle together. A lower score indicates it is doing so correctly, while a higher one means it makes more mistakes.