K Fold Cross Validation

K-fold cross-validation is a widely used resampling technique in machine learning for estimating model performance and selecting optimal hyperparameters, aiming to provide a robust and unbiased assessment of generalization ability. Current research focuses on addressing limitations such as computational cost (e.g., through early stopping strategies) and overoptimistic performance estimates, particularly in scenarios with limited data or multiple data sources. This rigorous evaluation method is crucial for ensuring reliable model selection across diverse applications, from medical diagnosis using deep learning models to improving the accuracy and reproducibility of results in fields like face recognition and human activity recognition.