community ensembles

Community ensembles are groups of different machine learning models working together to solve a problem. Instead of relying on a single model, they combine the strengths of multiple models to improve accuracy and robustness. By aggregating their predictions—through methods like voting or averaging—the ensemble can better handle complex data and reduce errors caused by individual models. This collaborative approach often results in more reliable and accurate outcomes, especially in challenging tasks such as classification, regression, or pattern recognition, making community ensembles a powerful tool in machine learning.