Many Heads Are Better Than One: The Case For Ensemble Learning

"The interests of truth require a diversity of opinions." Banks and lenders are increasingly turning to AI and machine learning to automate their core functions and make more accurate predictions in credit underwriting and fraud detection. ML practitioners can take advantage of a growing number of modeling algorithms, such as simple decision trees, random forests, gradient boosting machines, deep neural networks, and support vector machines. Each method has its strengths and weaknesses, which is why it often makes sense to combine ML algorithms to provide even greater predictive performance than any single ML method could provide on its own. This method of combining algorithms is known as ensembling.