A Good Fit in Machine Learning

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Ideally, you want to select a model at the sweet spot between underfitting and overfitting.
This is the goal but is very difficult to do in practice.
To understand this goal, we can look at the performance of a machine learning algorithm over time as it is learning training data. We can plot both the skill on the training data and the skill on a test dataset we have held back from the training process.
Over time, as the algorithm learns, the error for the model on the training data goes down and so does the error on the test dataset. If we train for too long, the performance on the training dataset may continue to decrease because the model is overfitting and learning the irrelevant detail and noise in the training dataset. At the same time, the error for the test set starts to rise again as the model’s ability to generalize decreases.
The sweet spot is the point just before the error on the test dataset starts to increase where the model has good skill on both the training dataset and the unseen test dataset.
You can perform this experiment with your favorite machine learning algorithms. This is often not useful technique in practice, because by choosing the stopping point for training using the skill on the test dataset it means that the test set is no longer “unseen” or a standalone objective measure. Some knowledge (a lot of useful knowledge) about that data has leaked into the training procedure.
There are two additional techniques you can use to help find the sweet spot in practice: resampling methods and a validation dataset.

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