Overfitting
Overfitting occurs when a machine learning model learns the training data too closely, including noise and patterns that do not generalize well to new, unseen data.
What is Overfitting?
An overfit model typically performs very well on its training data but performs worse on validation or test data. It can happen when a model is overly complex relative to the amount or quality of available data. Techniques such as regularization, early stopping, data augmentation, and cross-validation can help reduce overfitting.
Why is Overfitting Important?
Overfitting can make a model appear highly accurate during training while producing unreliable predictions in real-world situations. Detecting and reducing overfitting helps models generalize better and maintain more consistent performance on new data.
Common use cases
Overfitting is commonly addressed in neural networks, regression, classification, computer vision, natural language processing, and other machine learning applications.