Data Overfitting

Data overfitting occurs when a machine learning model learns not only the true patterns in the training data but also the random noise or outliers. This makes the model highly accurate on the training set but less effective on new, unseen data because it has essentially memorized the specific details rather than understanding general trends. Think of it like studying only one set of questions deeply; you'll do well on those but struggle with different questions. To prevent overfitting, techniques like simplifying the model or using more diverse data are employed to help the model generalize better.