# Overfitting Overfitting is when a machine learning model is fitting a data set too exactly and will cause it to fail when new data is introduced to it. In this case, the model fails (or simply has a high error rate) to deal with anything outside of the data set it was trained on. tl;dr It's as if the model memorized the data set and knows nothing else outside of it. Possible solutions: Split the data set into one used for training, and one used for validation. If the model generalizes well, it should have similar loss metrics across the training and validation data sets. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://wiki.omar.engineer/machine-learning/overfitting.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.