Gradient Boosting Classification Learner
mlr_learners_classif.gbm.Rd
Gradient Boosting Classification Algorithm.
Calls gbm::gbm()
from gbm.
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, gbm
Parameters
Id | Type | Default | Levels | Range |
distribution | character | bernoulli | bernoulli, adaboost, huberized, multinomial | - |
n.trees | integer | 100 | \([1, \infty)\) | |
interaction.depth | integer | 1 | \([1, \infty)\) | |
n.minobsinnode | integer | 10 | \([1, \infty)\) | |
shrinkage | numeric | 0.001 | \([0, \infty)\) | |
bag.fraction | numeric | 0.5 | \([0, 1]\) | |
train.fraction | numeric | 1 | \([0, 1]\) | |
cv.folds | integer | 0 | \((-\infty, \infty)\) | |
keep.data | logical | FALSE | TRUE, FALSE | - |
verbose | logical | FALSE | TRUE, FALSE | - |
n.cores | integer | 1 | \((-\infty, \infty)\) | |
var.monotone | untyped | - | - |
Initial parameter values
keep.data
is initialized toFALSE
to save memory.n.cores
is initialized to 1 to avoid conflicts with parallelization through future.
References
Friedman, H J (2002). “Stochastic gradient boosting.” Computational statistics & data analysis, 38(4), 367–378.
See also
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifGBM
Methods
Method importance()
The importance scores are extracted by gbm::relative.influence()
from
the model.
Returns
Named numeric()
.
Examples
# Define the Learner
learner = mlr3::lrn("classif.gbm")
print(learner)
#> <LearnerClassifGBM:classif.gbm>: Gradient Boosting
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: mlr3, mlr3extralearners, gbm
#> * Predict Types: [response], prob
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, missings, twoclass, weights
# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
#> Distribution not specified, assuming bernoulli ...
print(learner$model)
#> gbm::gbm(formula = f, data = data, keep.data = FALSE, n.cores = 1L)
#> A gradient boosted model with bernoulli loss function.
#> 100 iterations were performed.
#> There were 60 predictors of which 39 had non-zero influence.
print(learner$importance())
#> V12 V48 V11 V51 V45 V37 V21
#> 18.6844916 15.1283868 11.3279469 8.4894234 6.9518765 6.8098292 6.2173327
#> V49 V36 V4 V9 V55 V16 V23
#> 6.0563914 4.2961984 4.1661412 3.6647097 3.2065480 3.1293482 2.9744827
#> V31 V27 V40 V43 V3 V24 V20
#> 2.3682134 2.2984139 1.6630253 1.6187893 1.4696542 1.4652657 1.3673705
#> V54 V8 V32 V52 V6 V58 V29
#> 1.3279936 1.2763909 1.1721137 1.1395817 1.1151731 1.0229887 0.9938030
#> V19 V30 V56 V47 V53 V39 V46
#> 0.9934754 0.9314035 0.8154689 0.7930020 0.7410591 0.6559646 0.5317565
#> V44 V59 V13 V50 V1 V10 V14
#> 0.5249986 0.4833881 0.4061616 0.2990604 0.0000000 0.0000000 0.0000000
#> V15 V17 V18 V2 V22 V25 V26
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V28 V33 V34 V35 V38 V41 V42
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V5 V57 V60 V7
#> 0.0000000 0.0000000 0.0000000 0.0000000
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2028986