LearnerXgboost | R Documentation |
The LearnerXgboost
class is the interface to the xgboost
R package for
use with the mlexperiments
package.
Optimization metric: needs to be specified with the learner parameter
eval_metric
. The following options can be set via options()
:
"mlexperiments.optim.xgb.nrounds" (default: 5000L
)
"mlexperiments.optim.xgb.early_stopping_rounds" (default: 500L
)
"mlexperiments.xgb.print_every_n" (default: 50L
)
"mlexperiments.xgb.verbose" (default: FALSE
)
LearnerXgboost
can be used with
mlexperiments::MLTuneParameters
mlexperiments::MLCrossValidation
mlexperiments::MLNestedCV
mlexperiments::MLLearnerBase
-> LearnerXgboost
new()
Create a new LearnerXgboost
object.
LearnerXgboost$new(metric_optimization_higher_better)
metric_optimization_higher_better
A logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization.
A new LearnerXgboost
R6 object.
LearnerXgboost$new(metric_optimization_higher_better = FALSE)
clone()
The objects of this class are cloneable with this method.
LearnerXgboost$clone(deep = FALSE)
deep
Whether to make a deep clone.
xgboost::xgb.train()
, xgboost::xgb.cv()
# binary classification
library(mlbench)
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 |>
data.table::as.data.table() |>
na.omit()
seed <- 123
feature_cols <- colnames(dataset)[1:8]
param_list_xgboost <- expand.grid(
subsample = seq(0.6, 1, .2),
colsample_bytree = seq(0.6, 1, .2),
min_child_weight = seq(1, 5, 4),
learning_rate = seq(0.1, 0.2, 0.1),
max_depth = seq(1, 5, 4)
)
train_x <- model.matrix(
~ -1 + .,
dataset[, .SD, .SDcols = feature_cols]
)
train_y <- as.integer(dataset[, get("diabetes")]) - 1L
fold_list <- splitTools::create_folds(
y = train_y,
k = 3,
type = "stratified",
seed = seed
)
xgboost_cv <- mlexperiments::MLCrossValidation$new(
learner = mllrnrs::LearnerXgboost$new(
metric_optimization_higher_better = FALSE
),
fold_list = fold_list,
ncores = 2,
seed = 123
)
xgboost_cv$learner_args <- c(
as.list(
data.table::data.table(
param_list_xgboost[37, ],
stringsAsFactors = FALSE
),
),
list(
objective = "binary:logistic",
eval_metric = "logloss"
),
nrounds = 45L
)
xgboost_cv$performance_metric_args <- list(positive = "1")
xgboost_cv$performance_metric <- mlexperiments::metric("auc")
# set data
xgboost_cv$set_data(
x = train_x,
y = train_y
)
xgboost_cv$execute()
## ------------------------------------------------
## Method `LearnerXgboost$new`
## ------------------------------------------------
LearnerXgboost$new(metric_optimization_higher_better = FALSE)
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