Overfitting Detection Techniques

Overfitting detection involves identifying when a machine learning model has learned not just the underlying pattern but also the noise in training data, preventing it from performing well on new data. Common techniques include comparing performance on training versus validation datasets—if a model performs significantly better on training data but poorly on validation data, it’s likely overfitting. Cross-validation, where data is split multiple times to test consistency, helps detect overfitting. Additionally, monitoring metrics such as accuracy and loss during training; if they improve consistently on training data but plateau or worsen on validation data, overfitting is occurring.