Overfitting is a common problem in machine learning and deep learning where a model learns the training data too well, capturing noise and fluctuations rather than the underlying patterns. This results in high accuracy on the training dataset but poor performance on unseen data, indicating that the model has not generalized well. Essentially, the model becomes too complex, fitting the training data perfectly but failing to predict new data accurately ².

To identify overfitting, one can analyze learning curves, which show the training and validation loss over epochs. If the training loss continues to decrease while the validation loss starts to increase, it is a strong indicator of overfitting ³. Strategies to mitigate overfitting include simplifying the model, using regularization techniques, or employing dropout in neural networks ⁴.

Understanding and addressing overfitting is crucial for developing robust machine learning models that perform well on real-world data.