Overfitting occurs when a machine learning model learns not only the underlying patterns in the training data but also the noise or irrelevant information. This leads to a model that performs well on the training data but poorly on unseen or test data. Overfitting happens when the model becomes too complex, with too many parameters, relative to the amount of data it's trained on. This results in a lack of generalization, meaning the model cannot make accurate predictions on new, unseen data.

Common indicators of overfitting include a low error rate on the training set but a high error rate on the validation or test set. To mitigate overfitting, techniques like cross-validation, regularization (e.g., L1 or L2), reducing the complexity of the model, or using more training data are often employed.

Overfitting can be particularly problematic in real-world scenarios where the model must handle data it has never seen before. A balanced model should generalize well to new data while maintaining reasonable performance on the training set.

To learn more about handling overfitting and other essential concepts, enrolling in a machine learning data science course can provide deeper insights and hands-on experience.