Use ensemble techniques like boosting to train models sequentially, focusing on the errors made by previous models and capturing complex relationships in the data.

Use ensemble techniques like stacking to combine the predictions of multiple models, allowing each model to capture different aspects of the complex relationships.

Use feature engineering techniques to create new features that capture the complex relationships between existing features before training the ensemble models.

All of the above