By including bias, the variation in the final model's performance can be minimized. Combining the predictions from numerous models is the most typical approach to incorporate bias into the final model. Ensemble learning is the term for this. Ensemble learning can improve predictive performance in addition to reducing the variance of a final model's performance. Each contributing model must have skill, which means that the models must produce predictions that are better than random, while the prediction errors between the models must have a low correlation.