#CurrencyPairPrediction Hyperparameter Tuning in Forex ML Models Hyperparameter tuning is the process of optimizing the settings of a machine learning model to improve its predictive performance, which is especially critical in forex trading: Model Performance: Proper tuning can significantly enhance accuracy, reduce overfitting, and improve generalization to unseen data. Common Techniques: Methods like grid search, random search, and Bayesian optimization are used to find the best parameter combinations. Time-Sensitive Data: Forex models must account for time-series nature and avoid data leakage during tuning (e.g., using walk-forward validation). Trade-Offs: Tuning must balance complexity, speed, and robustness to market changes to avoid over-optimization on historical data. Effective hyperparameter tuning leads to more reliable and profitable forex trading models.