… We now make predictions using the best tuning parameter according to the cross validation
… In particular, we focus on the comparison with glmnet, as it has been carefully developed …

… We propose a two‐stage prediction model for heterogeneous treatment effects by … In the
first stage, a prognostic model to predict the baseline risk of the outcome. In the second stage, …

… predictions of partial models (for every run and every replication) for each algorithm; (2) by
calculating the weighted mean based on the AUC statistics … predicted values and predictions …

… of multi-omics data for the prediction of disease outcomes such as … most appropriate to derive
such prediction models. We aim to … prediction methods from machine learning and statistics …

… Yet, statistical developments for individual prediction of event have so far either focused on
… We used cv.glmnet() function (from the glmnet R package) with internal cross-validation to …

… We also include predict methods for ‘bess’ and ‘bess.one’ to make prediction on the new …
We also include glmnet as an approximate subset selection method and use the default cross-…

… To overcome the various challenges in crop yield prediction, in … weather indices wheat yield
prediction models was developed… “glmnet” package in R software was used to solve LASSO …

… The aim of this study was to evaluate the potentialities of statistical and artificial intelligence
… The LASSO was designed using the glmnet R function and the hyperparameter was tuned …

… The ‘glmnet’ R package was used to perform the LASSO … model and a nomogram to predict
the prognosis of patients with … Statistical analyses were performed using R Studio software …

… However, it is important to note that the cv.glmnet algorithm operates in a purely statistical
… to improve prediction accuracy. Therefore, after obtaining the output from cv.glmnet, we …