Why is it important to use ensemble techniques?

Ensemble techniques are important because they combine the predictions of multiple models to improve overall accuracy and robustness. By leveraging the strengths of different algorithms, ensemble methods can reduce overfitting and enhance generalization on unseen data. They also help mitigate the impact of individual model biases, leading to more reliable and consistent predictions. Overall, using ensemble techniques often results in superior performance compared to single models.