of interest. This algorithm provides us with a new class of loss-based cross-validated algorithms in prediction of …

When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many … learners. Motivated by this use of cross validation, we propose a new prediction method for creating a weighted combination of … in prediction which uses V-fold cross-validation to select weights to combine an initial set of candidate learners. In …

When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many … learners. Motivated by this use of cross validation, we propose a new prediction method for creating a weighted combination of … in prediction which uses V-fold cross-validation to select weights to combine an initial set of candidate learners. In …

of interest. This algorithm provides us with a new class of loss-based cross-validated algorithms in prediction of …

of right-censoring for the prediction of survival. Furthermore, we introduce how to incorporate bagging into the …

Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data …-generating distribution. In this article we introduce the "super learner", a prediction algorithm that applies any set of candidate learners …

Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data …-generating distribution. In this article we introduce the "super learner", a prediction algorithm that applies any set of candidate learners …

of right-censoring for the prediction of survival. Furthermore, we introduce how to incorporate bagging into the …