Overfitting is a prevalent problem in machine learning (ML). It refers to a situation where a model becomes too complex, leading to poor generalization capabilities. This phenomenon occurs when a model is trained on a limited dataset. Instead of learning general patterns applicable to new, unseen data, the model memorizes patterns specific to the training dataset. Consequently, the model can make highly accurate predictions on the training data but fails to perform well on validation or test datasets, as it cannot generalize its knowledge effectively.
To address or reduce overfitting, several strategies can be employed, such as regularization, cross-validation, and early stopping. Regularization simplifies the model's objective function by adding a penalty term. This encourages the model to avoid overly complex solutions and focus on more general patterns. Cross - validation involves dividing the data into multiple subsets (folds). The model is then trained and evaluated on each fold, providing a more comprehensive assessment of its performance across different data partitions. Early stopping is a technique where the model's performance during training is continuously monitored. Once the performance on the validation dataset starts to degrade, the training process is halted, preventing the model from overfitting further.
In summary, overfitting is a common yet significant issue in ML. It can severely affect a model's performance and accuracy. Therefore, it is crucial to closely monitor the model's performance during training and utilize methods like regularization, cross-validation, and early stopping to prevent or alleviate overfitting.