Final answer:
Ensemble techniques combine multiple models to improve predictive accuracy and are particularly useful in applications like fraud detection in banking, where the combined approach of different algorithms results in more reliable detection of fraudulent activities.
Step-by-step explanation:
An ensemble technique is a method used in predictive modeling where multiple models are combined to solve a particular computational intelligence problem. Ensemble techniques are useful in cases where a single model is not sufficient to produce accurate results. For instance, in the field of machine learning and data science, ensemble methods like Random Forests, Gradient Boosting Machines (GBM), or Stacking are employed to improve the predictive performance compared to individual models.
One example where ensemble techniques might be useful is in fraud detection systems in the banking sector. In this scenario, various algorithms can be used together to more accurately detect fraudulent transactions. The collective decision-making process of ensemble methods can lead to better generalization and less overfitting, which is crucial in such sensitive applications.