Overfitting in ML: Understanding and Avoiding the Pitfalls

Overfitting in machine learning is a common problem that occurs when a model is trained so much on the training dataset that it learns specific details about the training data that don't generalise well, and cause poor performance on new, unseen data. Overfitting can happen for a variety of reasons, but ultimately it leads to a model that is not able to generalize well and make accurate predictions on data it has not seen before. In this blog post, we will explore the causes of overfitting, the ways in which it can be prevented, and some strategies for dealing with overfitting if it occurs. We will talk about two of the main reasons for overfitting in this article: the model is overly complex, and training is run for too long. In fact, the combination of both of these situations is when overfitting is most prevalent!