gbt: Gradient Boosted Trees using XGBoost

In radiant.model: Model Menu for Radiant: Business Analytics using R and Shiny

Gradient Boosted Trees using XGBoost

Description

Gradient Boosted Trees using XGBoost

Usage

gbt(
  dataset,
  rvar,
  evar,
  type = "classification",
  lev = "",
  max_depth = 6,
  learning_rate = 0.3,
  min_split_loss = 0,
  min_child_weight = 1,
  subsample = 1,
  nrounds = 100,
  early_stopping_rounds = 10,
  nthread = 12,
  wts = "None",
  seed = NA,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame(),
  ...
)

Arguments

dataset	Dataset
rvar	The response variable in the model
evar	Explanatory variables in the model
type	Model type (i.e., "classification" or "regression")
lev	Level to use as the first column in prediction output
max_depth	Maximum 'depth' of tree
learning_rate	Learning rate (eta)
min_split_loss	Minimal improvement (gamma)
min_child_weight	Minimum number of instances allowed in each node
subsample	Subsample ratio of the training instances (0-1)
nrounds	Number of trees to create
early_stopping_rounds	Early stopping rule
nthread	Number of parallel threads to use. Defaults to 12 if available
wts	Weights to use in estimation
seed	Random seed to use as the starting point
data_filter	Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
arr	Expression to arrange (sort) the data on (e.g., "color, desc(price)")
rows	Rows to select from the specified dataset
envir	Environment to extract data from
...	Further arguments to pass to xgboost

Details

See https://radiant-rstats.github.io/docs/model/gbt.html for an example in Radiant

Value

A list with all variables defined in gbt as an object of class gbt

See Also

summary.gbt to summarize results

plot.gbt to plot results

predict.gbt for prediction

Examples

## Not run: 
gbt(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
gbt(titanic, "survived", c("pclass", "sex")) %>% str()

## End(Not run)
gbt(
  titanic, "survived", c("pclass", "sex"), lev = "Yes",
  early_stopping_rounds = 0, nthread = 1
) %>% summary()
gbt(
  titanic, "survived", c("pclass", "sex"),
  early_stopping_rounds = 0, nthread = 1
) %>% str()
gbt(
  titanic, "survived", c("pclass", "sex"),
  eval_metric = paste0("error@", 0.5 / 6), nthread = 1
) %>% str()
gbt(
  diamonds, "price", c("carat", "clarity"), type = "regression", nthread = 1
) %>% summary()

radiant.model documentation built on Oct. 16, 2023, 9:06 a.m.