Machine Learning Algorithms

Underfitting and overfitting

The purpose of a machine learning model is to approximate an unknown function that associates input elements to output ones (for a classifier, we call them classes). However, a training set is normally a representation of a global distribution, but it cannot contain all possible elements; otherwise the problem could be solved with a one-to-one association. In the same way, we don't know the analytic expression of a possible underlying function, therefore, when training, it's necessary to think about fitting the model but keeping it free to generalize when an unknown input is presented. Unfortunately, this ideal condition is not always easy to find and it's important to consider two different dangers:

• Underfitting ...