Automatically tunes an xgboost model using grid or bayesian optimization
auto_tune_xgboost(
.data,
formula,
tune_method = c("grid", "bayes"),
event_level = c("first", "second"),
n_fold = 5L,
n_iter = 100L,
seed = 1,
save_output = FALSE,
parallel = TRUE,
trees = tune::tune(),
min_n = tune::tune(),
mtry = tune::tune(),
tree_depth = tune::tune(),
learn_rate = tune::tune(),
loss_reduction = tune::tune(),
sample_size = tune::tune(),
stop_iter = tune::tune(),
counts = FALSE,
tree_method = c("auto", "exact", "approx", "hist", "gpu_hist"),
monotone_constraints = 0L,
num_parallel_tree = 1L,
lambda = 1,
alpha = 0,
scale_pos_weight = 1,
verbosity = 0L
)dataframe
formula
method of tuning. defaults to grid
for binary classification, which factor level is the positive class. specify "second" for second level
integer. n folds in resamples
n iterations for tuning (bayes); paramter grid size (grid)
seed
FASLE. If set to TRUE will write the output as an rds file
default TRUE; If set to TRUE, will enable parallel processing on resamples for grid tuning
# Trees (xgboost: nrounds) (type: integer, default: 500L)
Minimal Node Size (xgboost: min_child_weight) (type: integer, default: 2L); [typical range: 2-10] Keep small value for highly imbalanced class data where leaf nodes can have smaller size groups. Otherwise increase size to prevent overfitting outliers.
# Randomly Selected Predictors; defaults to .75; (xgboost: colsample_bynode) (type: numeric, range 0 - 1) (or type: integer if count = TRUE)
Tree Depth (xgboost: max_depth) (type: integer, default: 7L); Typical values: 3-10
Learning Rate (xgboost: eta) (type: double, default: 0.05); Typical values: 0.01-0.3
Minimum Loss Reduction (xgboost: gamma) (type: double, default: 1.0); range: 0 to Inf; typical value: 0 - 20 assuming low-mid tree depth
Proportion Observations Sampled (xgboost: subsample) (type: double, default: .75); Typical values: 0.5 - 1
# Iterations Before Stopping (xgboost: early_stop) (type: integer, default: 15L) only enabled if validation set is provided
if TRUE specify mtry as an integer number of cols. Default FALSE to specify mtry as fraction of cols from 0 to 1
xgboost tree_method. default is auto. reference: tree method docs
an integer vector with length of the predictor cols, of -1, 1, 0 corresponding to decreasing, increasing, and no constraint respectively for the index of the predictor col. reference: monotonicity docs.
should be set to the size of the forest being trained. default 1L
[default=.5] L2 regularization term on weights. Increasing this value will make model more conservative.
[default=.1] L1 regularization term on weights. Increasing this value will make model more conservative.
[default=1] Control the balance of positive and negative weights, useful for unbalanced classes. if set to TRUE, calculates sum(negative instances) / sum(positive instances). If first level is majority class, use values < 1, otherwise normally values >1 are used to balance the class distribution.
[default=1] Verbosity of printing messages. Valid values are 0 (silent), 1 (warning), 2 (info), 3 (debug).
workflow object
Default is to tune all 7 xgboost parameters. Individual parameter values can be optionally fixed to reduce tuning complexity.
iris %>%
framecleaner::create_dummies() -> iris1
#> 1 column(s) have become 3 dummy columns
iris1 %>%
tidy_formula(target = Petal.Length) -> petal_form
iris1 %>%
rsample::initial_split() -> iris_split
iris_split %>%
rsample::analysis() -> iris_train
iris_split %>%
rsample::assessment() -> iris_val
if (FALSE) {
iris_train %>%
auto_tune_xgboost(formula = petal_form, n_iter = 10,
parallel = FALSE, tune_method = "grid", mtry = .5) -> xgb_tuned
xgb_tuned %>%
parsnip::fit(iris_train) %>%
parsnip::extract_fit_engine() -> xgb_tuned_fit
xgb_tuned_fit %>%
tidy_predict(newdata = iris_val, form = petal_form) -> iris_val1
}