gbt {radiant.model} | R Documentation |
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()