Machine Learning, Uc Berkeley, Random Forest, Decision Tree, Ensemble Learning.
to obtain a more accurate result. However, putting so much effort into correcting errors tends to leave the ensemble, or forest, of decision trees prone to overfitting, because as you build more and more decision trees to correct errors made by the previous decision trees, even small errors and