Cross Validation

Cross validation is a statistical method used to estimate the skill of a machine learning model on unseen data by partitioning the dataset into multiple subsets. The model is trained on some subsets and validated on others, rotating through all available data to ensure that every observation is used for both training and testing.

This process provides a more reliable measure of model performance than a single train-test split, as it reduces the impact of random data partitioning. In finance, standard cross-validation must be adapted to account for the temporal nature of the data, often using walk-forward or expanding window approaches.

This ensures that the model is evaluated in a way that respects the chronological order of events, preventing look-ahead bias while providing a robust assessment of strategy effectiveness.