explore (version 1.3.4)

explain_xgboost: Explain a binary target using xgboost

Description

Based on the hyperparameters defined in the setup parameter, XGBoost hyperparameter-tuning is carried out using cross-validation. The best model is chosen and returned. As default, the function returns the feature-importance plot. To get the all outputs, use parameter out = "all"

Usage

explain_xgboost(
  data,
  target,
  log = TRUE,
  nthread = 1,
  setup = list(cv_nfold = 2, max_nrounds = 1000, early_stopping_rounds = 50, grid_xgboost
    = list(eta = c(0.3, 0.1, 0.01), max_depth = c(3, 5), gamma = 0, colsample_bytree =
    0.8, subsample = 0.8, min_child_weight = 1, scale_pos_weight = 1)),
  out = "plot"
)

Value

Plot of importance (if out = "plot")

Arguments

data

Data frame, must contain variable defined in target, but should not contain any customer-IDs or date/period columns

target

Target variable (must be binary 0/1, FALSE/TRUE, no/yes)

log

Log?

nthread

Number of threads used for training

setup

Setup of model

out

Output of the function: "plot" | "model" | "importance" | all"

Examples

data <- use_data_iris()
data$is_versicolor <- ifelse(data$Species == "versicolor", 1, 0)
data$Species <- NULL
explain_xgboost(data, target = is_versicolor, log = FALSE)