Overfitting
When a ML model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
Overfitting is a critical challenge in the field of machine learning, representing a scenario where a model becomes too complex, capturing not only the underlying patterns in the training data but also its random noise. This situation leads to poor generalization to unseen data, as the model is essentially memorizing the training dataset rather than learning the generalizable features. Overfitting can occur for various reasons, including having too many parameters relative to the number of observations, inadequate or excessively complex model architectures, or a lack of regularization. Combatting overfitting requires techniques such as cross-validation, regularization (like L1 and L2), pruning, or employing simpler models to ensure that the model retains its ability to generalize from the training data to real-world scenarios.
The concept of overfitting has been known since the early days of machine learning, but it became more prominently discussed with the rise of more complex models in the 1990s and 2000s. As models like neural networks gained popularity, the issue of overfitting became more apparent, prompting research into solutions such as dropout, regularization techniques, and validation methods to mitigate its effects.
While it's challenging to attribute the concept of overfitting to specific individuals due to its foundational nature in statistics and machine learning, notable figures such as Geoffrey Hinton and Andrew Ng have contributed significantly to developing methods for preventing overfitting, especially in the context of deep learning.