Table 2 Basic learning algorithm contained in each integration model (n = 3751)
Model | Ensemble 1 (Em1) | Ensemble 2 (Em2 | Logistic |
|---|---|---|---|
RandomForest | X | √ | X |
Classification and Expression Training (caret) | √ | √ | X |
Generalized additive model (gam) | √ | √ | X |
AIC stepwise regression (step) | X | √ | X |
Ridge regression | √ | √ | X |
Regularization regression (glmnet) | X | √ | X |
Xgboost | √ | √ | X |
Non-negative least squares regression (nnls) | X | √ | X |
Support vector machine (svm) | √ | √ | X |