gbm_baseline: Gradient boosting machine baseline model function.
In LBNL-ETA/RMV2.0: M&V2.0 Using R and Shiny

gbm_baseline This function builds a baseline model using gradient boosting machine algorithm.

gbm_baseline(train_path = NULL, pred_path = NULL, days_off_path = NULL,
  train_Data = NULL, pred_Data = NULL, k_folds = 5,
  variables = c("Temp", "tow"), ncores = parallel::detectCores(logical = F),
  cv_blocks = "weeks", iter = seq(from = 50, to = 300, by = 25),
  depth = c(3:7), lr = c(0.05, 0.1), subsample = c(0.5),
  verbose = FALSE)

`train_path`	The path of the file from which the training data are to be read.
`pred_path`	The path of the file from which the prediction data are to be read.
`days_off_path`	The path of the file from which the date data of days off (e.g., holidays) are to be read.
`train_Data`	A dataframe, of the training period, where the columns correspond to the time steps (time), the energy load (eload) and to the Temperature (Temp).
`pred_Data`	A dataframe, of the prediction period, where the columns correspond to the time steps (time), the energy load (eload) and to the Temperature (Temp).
`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 correspond to the standard k-fold cross validation technique.
`iter`	A vector with combination of the number of iterations.
`depth`	A vector with combination of the maximum depths.
`lr`	A vector with combination of the learning rates.
`subsample`	A vector with combination of subsamples.

a gbm_baseline object, which alist with the following components:

gbm_model: an object that has been created by the function xgboost, and which correspond to the optimal gbm model.
train: a dataframe that correspond to the training data after the cleaning and filtering function were applied.
fitting: the fitted values.
goodness_of_fit: a dataframe that contains the goodness of fitting metrics.
gbm_cv_results: a dataframe the training accuracy metrics (R2, RMSE and CVRMSE) and values of the tuning hype-parameters.
tuned_parameters: a list of the best hyper-parameters
pred: a dataframe that correspond to the prediction data after the cleaning and filtering function were applied.
prediction: the predicted values.