tidy_xgboost: tidy xgboost

In autostats: Auto Stats

tidy xgboost

Description

Accepts a formula to run an xgboost model. Automatically determines whether the formula is for classification or regression. Returns the xgboost model.

Usage

tidy_xgboost(
  .data,
  formula,
  ...,
  mtry = 1,
  trees = 15L,
  min_n = 1L,
  tree_depth = 6L,
  learn_rate = 0.3,
  loss_reduction = 0,
  sample_size = 1,
  stop_iter = 10L,
  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,
  validate = TRUE
)

Arguments

.data	dataframe
formula	formula
...	additional parameters to be passed to set_engine
mtry	# Randomly Selected Predictors (xgboost: colsample_bynode) (type: numeric, range 0 - 1) (or type: integer if count = TRUE)
trees	# Trees (xgboost: nrounds) (type: integer, default: 15L)
min_n	Minimal Node Size (xgboost: min_child_weight) (type: integer, default: 1L); [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.
tree_depth	Tree Depth (xgboost: max_depth) (type: integer, default: 6L); Typical values: 3-10
learn_rate	Learning Rate (xgboost: eta) (type: double, default: 0.3); Typical values: 0.01-0.3
loss_reduction	Minimum Loss Reduction (xgboost: gamma) (type: double, default: 0.0); range: 0 to Inf; typical value: 0 - 20 assuming low-mid tree depth
sample_size	Proportion Observations Sampled (xgboost: subsample) (type: double, default: 1.0); Typical values: 0.5 - 1
stop_iter	# Iterations Before Stopping (xgboost: early_stop) (type: integer, default: 15L) only enabled if validation set is provided
counts	if TRUE specify mtry as an integer number of cols. Default FALSE to specify mtry as fraction of cols from 0 to 1
tree_method	xgboost tree_method. default is auto. reference: tree method docs
monotone_constraints	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.
num_parallel_tree	should be set to the size of the forest being trained. default 1L
lambda	[default=1] L2 regularization term on weights. Increasing this value will make model more conservative.
alpha	[default=0] L1 regularization term on weights. Increasing this value will make model more conservative.
scale_pos_weight	[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.
verbosity	[default=1] Verbosity of printing messages. Valid values are 0 (silent), 1 (warning), 2 (info), 3 (debug).
validate	default TRUE. report accuracy metrics on a validation set.

Details

In binary classification the target variable must be a factor with the first level set to the event of interest. A higher probability will predict the first level.

reference for parameters: xgboost docs

Value

xgb.Booster model

Examples

options(rlang_trace_top_env = rlang::current_env())


# regression on numeric variable

iris %>%
 framecleaner::create_dummies(Species) -> iris_dummy

iris_dummy %>%
 tidy_formula(target= Petal.Length) -> petal_form

iris_dummy %>%
 tidy_xgboost(
   petal_form,
   trees = 20,
   mtry = .5
 )  -> xg1


xg1 %>%
 tidy_predict(newdata = iris_dummy, form = petal_form) -> iris_preds

iris_preds %>%
 eval_preds()

autostats documentation built on Nov. 10, 2022, 6:13 p.m.