Which technique involves combining predictions from multiple models?

Ensemble methods are a powerful technique in machine learning that involve combining predictions from multiple models to improve the overall performance and robustness of the predictions. The idea behind ensemble methods is that by aggregating the outputs of different models, individual errors can be minimized, and the strength of various models can be leveraged to produce more accurate predictions.

Ensemble methods can take various forms, such as bagging and boosting, which are designed to either reduce variance (as in bagging methods like Random Forests) or to sequentially improve model accuracy (as in boosting methods like Gradient Boosting). By utilizing the diversity of multiple models, ensemble methods are generally able to outperform single-model approaches, leading to better generalization on unseen data.

In contrast, Random Forest and Gradient Boosting are specific types of ensemble methods; they’re both powerful techniques but fall under the broader category of ensemble methods. Clustering, on the other hand, is an entirely different technique focused on grouping data points based on similarity rather than combining predictions from multiple models.