Description Usage Arguments Examples
XGBoost implementation for stream water temperature prediction including Bayesian hyperparameter optimization. All results are stored automatically in the folder catchment/model_name.
1 2 3 4 5 6 7 8 9 10 11 | wt_xgboost(
train_data,
test_data = NULL,
catchment = NULL,
cv_mode = "repCV",
model_name = NULL,
no_cores = parallel::detectCores() - 1,
seed = NULL,
n_iter = 40,
n_random_initial_points = 20
)
|
train_data |
Data frame containing training data created by using wt_preprocessing() |
test_data |
Data frame containing test data created by using wt_preprocessing() |
catchment |
Catchment name as string, used for storing results in current working directory. |
cv_mode |
Cross-validation mode. Can either be "repCV" for a 5times repeated 10-fold CV or "timeseriesCV" for a timeslice CV using intial window=730, horizon=90 and skip=60. |
model_name |
Name of this particular model run as string, used for storing results in the catchment folder. |
no_cores |
Number of cores used for computation. If NULL parallel::detectCores() - 1 is applied. |
seed |
Random seed. |
n_iter |
Number of iteration steps for bayesian hyperparameter optimization. |
n_random_initial_points |
Number of sampled initial random points for bayesian hyperparameter optimization |
1 2 3 4 5 6 7 8 9 | ## Not run:
data(test_catchment)
wt_preprocess(test_catchment)
train_data <- feather::read_feather("test_catchment/train_data.feather")
test_data <- feather::read_feather("test_catchment/test_data.feather")
wt_xgboost(train_data, test_data, "test_catchment", "repCV", "standard_xgboost")
## End(Not run)
|
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