Extreme Gradient Boosting Classification Learner
Source:R/LearnerClassifXgboost.R
mlr_learners_classif.xgboost.RdeXtreme 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.
Note that using the watchlist parameter directly will lead to problems when wrapping this Learner in a
mlr3pipelines GraphLearner as the preprocessing steps will not be applied to the data in the watchlist.
Note
To compute on GPUs, you first need to compile xgboost yourself and link against CUDA. See https://xgboost.readthedocs.io/en/stable/build.html#building-with-gpu-support.
Initial parameter values
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.
nroundsneeds 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.
Early stopping
Early stopping can be used to find the optimal number of boosting rounds.
The early_stopping_set parameter controls which set is used to monitor the performance.
Set early_stopping_set = "test" to monitor the performance of the model on the test set while training.
The test set for early stopping can be set with the "test" row role in the mlr3::Task.
Additionally, the range must be set in which the performance must increase with early_stopping_rounds and the maximum number of boosting rounds with nrounds.
While resampling, the test set is automatically applied from the mlr3::Resampling.
Not that using the test set for early stopping can potentially bias the performance scores.
See the section on early stopping in the examples.
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 | untyped | list | - | |
| 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 | \([1, \infty)\) | |
| early_stopping_set | character | none | none, train, test | - |
| eta | numeric | 0.3 | \([0, 1]\) | |
| eval_metric | untyped | - | - | |
| feature_selector | character | cyclic | cyclic, shuffle, random, greedy, thrifty | - |
| feval | untyped | - | ||
| gamma | numeric | 0 | \([0, \infty)\) | |
| grow_policy | character | depthwise | depthwise, lossguide | - |
| interaction_constraints | untyped | - | - | |
| iterationrange | untyped | - | - | |
| 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 | untyped | 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 | untyped | 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 | untyped | - | ||
| save_period | integer | NULL | \([0, \infty)\) | |
| scale_pos_weight | numeric | 1 | \((-\infty, \infty)\) | |
| skip_drop | numeric | 0 | \([0, 1]\) | |
| 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 | untyped | - | - | |
| verbose | integer | 1 | \([0, 2]\) | |
| watchlist | untyped | - | ||
| xgb_model | untyped | - |
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. doi: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.nnet,
mlr_learners_regr.ranger,
mlr_learners_regr.svm,
mlr_learners_regr.xgboost
Super classes
mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifXgboost
Methods
Method importance()
The importance scores are calculated with xgboost::xgb.importance().
Returns
Named numeric().
Examples
if (requireNamespace("xgboost", quietly = TRUE)) {
learner = mlr3::lrn("classif.xgboost")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClassifXgboost:classif.xgboost>
#> * Model: -
#> * Parameters: nrounds=1, nthread=1, verbose=0, early_stopping_set=none
#> * Packages: mlr3, mlr3learners, xgboost
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: hotstart_forward, importance, missings, multiclass,
#> twoclass, weights
#> [1] "alpha" "approxcontrib"
#> [3] "base_score" "booster"
#> [5] "callbacks" "colsample_bylevel"
#> [7] "colsample_bynode" "colsample_bytree"
#> [9] "disable_default_eval_metric" "early_stopping_rounds"
#> [11] "early_stopping_set" "eta"
#> [13] "eval_metric" "feature_selector"
#> [15] "feval" "gamma"
#> [17] "grow_policy" "interaction_constraints"
#> [19] "iterationrange" "lambda"
#> [21] "lambda_bias" "max_bin"
#> [23] "max_delta_step" "max_depth"
#> [25] "max_leaves" "maximize"
#> [27] "min_child_weight" "missing"
#> [29] "monotone_constraints" "normalize_type"
#> [31] "nrounds" "nthread"
#> [33] "ntreelimit" "num_parallel_tree"
#> [35] "objective" "one_drop"
#> [37] "outputmargin" "predcontrib"
#> [39] "predictor" "predinteraction"
#> [41] "predleaf" "print_every_n"
#> [43] "process_type" "rate_drop"
#> [45] "refresh_leaf" "reshape"
#> [47] "seed_per_iteration" "sampling_method"
#> [49] "sample_type" "save_name"
#> [51] "save_period" "scale_pos_weight"
#> [53] "skip_drop" "strict_shape"
#> [55] "subsample" "top_k"
#> [57] "training" "tree_method"
#> [59] "tweedie_variance_power" "updater"
#> [61] "verbose" "watchlist"
#> [63] "xgb_model"
# Train learner with early stopping on spam data set
task = tsk("spam")
# Split task into training and test set
split = partition(task, ratio = 0.8)
task$set_row_roles(split$test, "test")
# Set early stopping parameter
learner = lrn("classif.xgboost",
nrounds = 1000,
early_stopping_rounds = 100,
early_stopping_set = "test"
)
# Train learner with early stopping
learner$train(task)