When your model is too smart for its own good and memorizes the training data instead of learning useful patterns.
Overfitting is a phenomenon in machine learning and data science where a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data. This occurs when the model is excessively complex, having too many parameters relative to the number of observations. As a result, while the model may perform exceptionally well on the training dataset, it fails to generalize to unseen data, leading to poor predictive performance. Overfitting is particularly critical in fields such as artificial intelligence, where the ability to generalize from training data to real-world applications is essential.
Overfitting is often identified through techniques such as cross-validation, where the model's performance is evaluated on a separate validation dataset. It is important for data scientists, machine learning engineers, and data analysts to recognize and mitigate overfitting to ensure that their models are robust and reliable. Common strategies to avoid overfitting include simplifying the model, using regularization techniques, and employing dropout in neural networks.
It's like training for a marathon by only running in your living room; you might ace the treadmill, but good luck on the actual pavement!
Overfitting was first recognized in the early days of statistical modeling, but it gained significant attention in the 1990s as machine learning began to flourish, leading to the development of various techniques aimed at preventing this common pitfall.
