The process of selecting the best configuration parameters (hyperparameters) for a learning model before training. Hyperparameters include learning rate, network layers/neurons, regularization strength, etc. In forex AI, tuning hyperparameters is critical: for example, choosing how many layers an LSTM has or what learning rate to use can greatly affect the robot’s predictive accuracy. Proper tuning is often done by grid search or automated methods, and it is essential for minimizing the model’s loss function and avoiding overfitting.

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