1. to fit a statistical or machine learning model too closely to training data, causing it to perform poorly on new, unseen data
Usage: technical; computing/statistics

1. the condition or result of fitting a model too closely to training data
Usage: technical; computing/statistics

Examples

• The neural network began to overfit after training on the same dataset for too many epochs.
• To prevent overfitting, data scientists use cross-validation and regularization techniques.
• The model showed high accuracy on training data but suffered from overfitting when tested on new examples.
• Adding dropout layers can help reduce overfitting in deep learning models.
• We detected overfitting when the validation error started increasing while training error continued to decrease.