small ensemble

A small ensemble in machine learning refers to a collection of a limited number of models working together to make predictions. Instead of relying on a single model, multiple models—each trained slightly differently—combine their outputs to improve accuracy and robustness. By aggregating diverse opinions, a small ensemble reduces errors that might occur if a single model were used alone. This approach balances the benefits of improved performance with manageable complexity, making it effective in scenarios where computational resources or interpretability are important considerations.