Description: Overfitting is a phenomenon that occurs in the training of machine learning models, including a wide range of algorithms and frameworks. It refers to the situation where a model is excessively trained on a specific dataset, resulting in exceptional performance on that data but poor generalization capability on unseen data. This happens because the model learns not only the general characteristics of the data but also the noise and specific peculiarities of the training set. As a consequence, the model may fail to make accurate predictions in real-world situations where the data may vary. Overfitting is a critical issue in the development of artificial intelligence models, as it can lead to erroneous decisions and a lack of robustness in practical applications. To mitigate this problem, various techniques are employed, such as regularization, cross-validation, and early stopping, which help balance model complexity and generalization capability. Understanding and managing overfitting is essential for the effective development of deep learning models across various applications.
