caret interface for greedyMSE. greedyMSE works well when you want an ensemble that will never be worse than any single predictor in the dataset. It does not use an intercept and it does not allow for negative coefficients. This makes it highly constrained and in general does not work well on standard classification and regression problems. However, it does work well in the case of: * The predictors are highly correlated with each other * The predictors are highly correlated with the model * You expect or want positive only coefficients In the worse case, this method will select one input and use that, but in many other cases it will return a positive, weighted average of the inputs. Since it never uses negative weights, you never get into a scenario where one model is weighted negative and on new data you get were predictions because a correlation changed. Since this model will always be a positive weighted average of the inputs, it will rarely do worse than the individual models on new data.