Skeptical about ensemble methods in data mining? You can boost their predictive accuracy with a few strategic approaches:

• Cross-validation: Use techniques like k-fold cross-validation to assess model performance on different data subsets.

• Diverse models: Combine diverse algorithms to reduce overfitting and improve generalization.

• Hyperparameter tuning: Optimize parameters using grid search or random search for better model precision.

How do you ensure the accuracy of ensemble methods? Share your strategies.