tidy_xgboost {autostats}

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 = 0.75,
  trees = 500L,
  min_n = 2L,
  tree_depth = 7L,
  learn_rate = 0.05,
  loss_reduction = 1,
  sample_size = 0.75,
  stop_iter = 15L,
  counts = FALSE,
  tree_method = c("auto", "exact", "approx", "hist", "gpu_hist"),
  monotone_constraints = 0L,
  num_parallel_tree = 1L,
  lambda = 0.5,
  alpha = 0.1,
  scale_pos_weight = 1,
  verbosity = 0L,
  validate = TRUE,
  booster = c("gbtree", "gblinear")
)

Arguments

.data

dataframe

formula

formula

...

additional parameters to be passed to set_engine

mtry

# Randomly Selected Predictors; defaults to .75; (xgboost: colsample_bynode) (type: numeric, range 0 - 1) (or type: integer if count = TRUE)

trees

# Trees (xgboost: nrounds) (type: integer, default: 500L)

min_n

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.

tree_depth

Tree Depth (xgboost: max_depth) (type: integer, default: 7L); Typical values: 3-10

learn_rate

Learning Rate (xgboost: eta) (type: double, default: 0.05); Typical values: 0.01-0.3

loss_reduction

Minimum Loss Reduction (xgboost: gamma) (type: double, default: 1.0); range: 0 to Inf; typical value: 0 - 20 assuming low-mid tree depth

sample_size

Proportion Observations Sampled (xgboost: subsample) (type: double, default: .75); 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=.5] L2 regularization term on weights. Increasing this value will make model more conservative.

alpha

[default=.1] 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.

booster

defaults to 'gbtree' for tree boosting but can be set to 'gblinear'

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()