Overfitting happens when a model is trained too well on its training data, capturing noise and details that do not generalize to new data. It means the model performs excellently on training data but poorly on unseen data. Overfitting is a common issue in machine learning, leading to unreliable predictions and reduced accuracy.

AI and Overfitting: What to Expect

AI helps tackle overfitting by employing techniques like cross-validation, regularization, and pruning. These methods ensure your models generalize better, providing more accurate predictions. In the future, AI will continue to improve, reducing the risk of overfitting. Expect smarter algorithms and advanced techniques to keep your models robust and reliable.