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Gradient Boosting Classification Algorithm. Calls gbm::gbm() from gbm.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.gbm")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3, mlr3extralearners, gbm

Parameters

IdTypeDefaultLevelsRange
distributioncharacterbernoullibernoulli, adaboost, huberized, multinomial-
n.treesinteger100\([1, \infty)\)
interaction.depthinteger1\([1, \infty)\)
n.minobsinnodeinteger10\([1, \infty)\)
shrinkagenumeric0.001\([0, \infty)\)
bag.fractionnumeric0.5\([0, 1]\)
train.fractionnumeric1\([0, 1]\)
cv.foldsinteger0\((-\infty, \infty)\)
keep.datalogicalFALSETRUE, FALSE-
verboselogicalFALSETRUE, FALSE-
n.coresinteger1\((-\infty, \infty)\)
var.monotoneuntyped--

Initial parameter values

  • keep.data is initialized to FALSE 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

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGBM

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method importance()

The importance scores are extracted by gbm::relative.influence() from the model.

Usage

LearnerClassifGBM$importance()

Returns

Named numeric().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGBM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.gbm")
print(learner)
#> 
#> ── <LearnerClassifGBM> (classif.gbm): Gradient Boosting ────────────────────────
#> • Model: -
#> • Parameters: keep.data=FALSE, n.cores=1
#> • Packages: mlr3, mlr3extralearners, and gbm
#> • Predict Types: [response] and prob
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, twoclass, and weights
#> • Other settings: use_weights = 'use'

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = 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())
#>        V11        V37        V45        V12        V48        V55        V51 
#> 23.5815039 11.4462457  9.0115089  8.2640928  6.6770570  6.4930100  5.3268483 
#>        V36        V31        V44        V10        V16         V4        V52 
#>  5.1805523  4.5324090  4.2570807  4.1467486  3.9196451  3.7976187  2.8610125 
#>        V20        V34        V46         V5        V39        V57        V15 
#>  2.8146249  2.1330937  2.1293190  2.1188198  2.1148052  1.9423557  1.7795206 
#>        V21        V43        V35        V49        V17        V47        V33 
#>  1.4437540  1.2751104  1.1839336  1.1712199  0.9623875  0.9109990  0.8510703 
#>         V6        V27        V23        V25        V28        V29        V24 
#>  0.8410606  0.8125996  0.7325085  0.6734647  0.5340257  0.4954861  0.4476179 
#>        V13        V14         V8        V53         V1        V18        V19 
#>  0.4067810  0.3936904  0.3646881  0.3534359  0.0000000  0.0000000  0.0000000 
#>         V2        V22        V26         V3        V30        V32        V38 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V40        V41        V42        V50        V54        V56        V58 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V59        V60         V7         V9 
#>  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 
#>  0.1014493