et's compare the algorithms you have previously fitted individually in this workshop: lasso, decision tree, random forest, and xgboost. We will add two other algorithms for fun: logistic regression and the mean of our Y response variable. 

- **Risk:** is a performance estimate - it's the average loss, and loss is how far off the prediction was for an individual observation. Lower is better! 
- **Discrete.SL:** the singled best-performing algorithm. 
- **Super Learner:** the weighted ensemble average algori