Ensemble methods are a powerful technique in machine learning that combine multiple models to produce a single, robust, and accurate prediction. These methods are especially valuable in nanotechnology due to the complexity and variability of nanoscale materials and processes. By aggregating the predictions of multiple models, ensemble methods can mitigate the limitations of individual models and enhance overall performance.
