How Does K Fold Cross Validation Improve Model Performance?
By using K fold cross validation, researchers can identify if a model is overfitting or underfitting. Overfitting happens when a model performs well on training data but poorly on unseen data. Underfitting occurs when a model cannot capture the underlying trend of the data. K fold cross validation exposes the model to multiple training and validation sets, providing a more accurate estimate of its performance and thereby guiding adjustments to improve its generalizability.
