Wrapper to generate multi-response predictive models.
mrIMLpredicts( X, Y, Model, balance_data = "no", mode = "regression", transformY = "log", dummy = FALSE, tune_grid_size = 10, k = 10, seed = sample.int(1e+08, 1) )
X | A |
---|---|
Y | A |
Model | 1 A |
balance_data | A |
mode |
|
dummy | A |
tune_grid_size | A |
k | A |
This function produces yhats that used in all subsequent functions. This function fits separate classification/regression models for each response variable in a data set. Rows in X (features) have the same id (host/site/population) as Y. Class imbalance can be a real issue for classification analyses. Class imbalance can be addressed for each response variable using 'up' (upsampling using ROSE bootstrapping), 'down' (downsampling) or 'no' (no balancing of classes).