Description Usage Arguments Value
gbm_tune
splits the data into k folds by randomly selecting blocks of data,
where each block correspond to a calendar day.
1 2 |
Data |
A dataframe. |
k_folds |
An integer that corresponds to the number of CV folds. |
variables |
A vector that contains the names of the variables that will be considered by the function as input variables. |
ncores |
Number of threads used for the parallelization of the cross validation |
cv_blocks |
type of blocks for the cross validation; Default is "none", which corresponds to the standard cross validation technique |
iter |
The search grid combination of the number of iterations |
depth |
The search grid combination of the maximum depths |
lr |
The search grid combination of the learning rates |
a list with the two following components:
a dataframe the training accuracy metrics (R2, RMSE and CVRMSE) and values of the tuning hype-parameters
a list of the best hyper-parameters
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