What is overfitting in machine learning?

Overfitting in machine learning represents a scenario where a model exhibits high performance on the training dataset but fails to generalize effectively to new or unseen data. This phenomenon arises when the model becomes overly complex, capturing noise and random variations in the training data as if they were genuine patterns. Recognizable indications of overfitting include a notable divergence between the model’s performance on the training set, characterized by exceptionally low error rates, and its performance on validation or test data, where errors are disproportionately high. Overfitting can manifest due to various factors, such as employing excessively complex models with more parameters than the available data can support, inadequate regularization techniques to mitigate the influence of noise, or utilizing insufficient or poor-quality training data that fails to adequately represent the underlying distribution. Additionally, the inadvertent leakage of information from validation or test sets into the training process can exacerbate overfitting tendencies. To combat overfitting, practitioners employ various strategies, including regularization methods like L1 or L2 regularization, cross-validation, early stopping, dropout, and the use of simpler models. These techniques help temper the model’s tendency to fit noise in the data, encouraging it to capture underlying patterns that generalize more effectively to new, unseen data.