👉 Holdouts are a method used in machine learning to prevent overfitting, where the model performs well on training data but poorly on new or unseen test data. This is often done by selecting a subset of the training data that is not used during training and using it to train the model until it has learned from the training data. In other words, holdouts are used to evaluate the performance of a machine learning model on new data and determine if it's overfitting or not. The idea
