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mlr3learners (version 0.5.1)

mlr_learners_classif.xgboost: Extreme Gradient Boosting Classification Learner

Description

eXtreme Gradient Boosting classification. Calls xgboost::xgb.train() from package xgboost.

If not specified otherwise, the evaluation metric is set to the default "logloss" for binary classification problems and set to "mlogloss" for multiclass problems. This was necessary to silence a deprecation warning.

Arguments

Custom mlr3 defaults

  • nrounds:

    • Actual default: no default.

    • Adjusted default: 1.

    • Reason for change: Without a default construction of the learner would error. Just setting a nonsense default to workaround this. nrounds needs to be tuned by the user.

  • nthread:

    • Actual value: Undefined, triggering auto-detection of the number of CPUs.

    • Adjusted value: 1.

    • Reason for change: Conflicting with parallelization via future.

  • verbose:

    • Actual default: 1.

    • Adjusted default: 0.

    • Reason for change: Reduce verbosity.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.xgboost")
lrn("classif.xgboost")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “logical”, “integer”, “numeric”

  • Required Packages: mlr3, mlr3learners, xgboost

Parameters

Id Type Default Levels Range
alpha numeric 0 \([0, \infty)\)
approxcontrib logical FALSE TRUE, FALSE -
base_score numeric 0.5 \((-\infty, \infty)\)
booster character gbtree gbtree, gblinear, dart -
callbacks list NULL -
colsample_bylevel numeric 1 \([0, 1]\)
colsample_bynode numeric 1 \([0, 1]\)
colsample_bytree numeric 1 \([0, 1]\)
disable_default_eval_metric logical FALSE TRUE, FALSE -
early_stopping_rounds integer NULL \([1, \infty)\)
eta numeric 0.3 \([0, 1]\)
eval_metric list - -
feature_selector character cyclic cyclic, shuffle, random, greedy, thrifty -
feval list NULL -
gamma numeric 0 \([0, \infty)\)
grow_policy character depthwise depthwise, lossguide -
interaction_constraints list - -
iterationrange list - -
lambda numeric 1 \([0, \infty)\)
lambda_bias numeric 0 \([0, \infty)\)
max_bin integer 256 \([2, \infty)\)
max_delta_step numeric 0 \([0, \infty)\)
max_depth integer 6 \([0, \infty)\)
max_leaves integer 0 \([0, \infty)\)
maximize logical NULL TRUE, FALSE -
min_child_weight numeric 1 \([0, \infty)\)
missing numeric NA \((-\infty, \infty)\)
monotone_constraints list 0 -
normalize_type character tree tree, forest -
nrounds integer - \([1, \infty)\)
nthread integer 1 \([1, \infty)\)
ntreelimit integer NULL \([1, \infty)\)
num_parallel_tree integer 1 \([1, \infty)\)
objective list binary:logistic -
one_drop logical FALSE TRUE, FALSE -
outputmargin logical FALSE TRUE, FALSE -
predcontrib logical FALSE TRUE, FALSE -
predictor character cpu_predictor cpu_predictor, gpu_predictor -
predinteraction logical FALSE TRUE, FALSE -
predleaf logical FALSE TRUE, FALSE -
print_every_n integer 1 \([1, \infty)\)
process_type character default default, update -
rate_drop numeric 0 \([0, 1]\)
refresh_leaf logical TRUE TRUE, FALSE -
reshape logical FALSE TRUE, FALSE -
seed_per_iteration logical FALSE TRUE, FALSE -
sampling_method character uniform uniform, gradient_based -
sample_type character uniform uniform, weighted -
save_name list NULL -
save_period integer NULL \([0, \infty)\)
scale_pos_weight numeric 1 \((-\infty, \infty)\)
sketch_eps numeric 0.03 \([0, 1]\)
skip_drop numeric 0 \([0, 1]\)
single_precision_histogram logical FALSE TRUE, FALSE -
strict_shape logical FALSE TRUE, FALSE -
subsample numeric 1 \([0, 1]\)
top_k integer 0 \([0, \infty)\)
training logical FALSE TRUE, FALSE -
tree_method character auto auto, exact, approx, hist, gpu_hist -
tweedie_variance_power numeric 1.5 \([1, 2]\)
updater list - -
verbose integer 1 \([0, 2]\)
watchlist list NULL -
xgb_model list NULL -

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifXgboost

Methods

Public methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifXgboost$new()

Method importance()

The importance scores are calculated with xgboost::xgb.importance().

Usage

LearnerClassifXgboost$importance()

Returns

Named numeric().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifXgboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Chen, Tianqi, Guestrin, Carlos (2016). “Xgboost: A scalable tree boosting system.” In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 785--794. ACM. 10.1145/2939672.2939785.

See Also

  • Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

  • Package mlr3extralearners for more learners.

  • Dictionary of Learners: mlr_learners

  • as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

  • mlr3pipelines to combine learners with pre- and postprocessing steps.

  • Extension packages for additional task types:

    • mlr3proba for probabilistic supervised regression and survival analysis.

    • mlr3cluster for unsupervised clustering.

  • mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost, mlr_learners_surv.cv_glmnet, mlr_learners_surv.glmnet, mlr_learners_surv.ranger, mlr_learners_surv.xgboost

Examples

Run this code
# NOT RUN {
if (requireNamespace("xgboost", quietly = TRUE)) {
  learner = mlr3::lrn("classif.xgboost")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
# }

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