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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$model)
#> gbm::gbm(formula = f, data = data, keep.data = FALSE, n.cores = 1L)
#> A gradient boosted model with bernoulli loss function.
#> 100 iterations were performed.
#> There were 60 predictors of which 36 had non-zero influence.
print(learner$importance())
#>        V11        V49        V45        V12        V31        V10        V21 
#> 14.2900536  9.7027787  9.5716650  9.4574296  9.3205547  7.7952053  6.2110727 
#>        V16        V36        V28        V48        V55         V5        V59 
#>  5.8096983  5.4213001  5.3933832  5.1535586  4.2267556  3.9286642  3.8328984 
#>         V4        V52        V19        V51        V54         V1        V38 
#>  3.4257509  2.8613396  2.8363345  2.7037293  2.5682358  2.5311463  2.5172394 
#>        V37        V44        V57        V40        V27        V29        V15 
#>  2.4606833  2.3196345  1.5828502  1.3594812  1.1278129  1.1166633  1.1090623 
#>        V34        V20        V39         V8        V23        V33         V9 
#>  1.0492194  0.8915011  0.6435603  0.6272934  0.5673393  0.4794080  0.4385280 
#>         V6        V13        V14        V17        V18         V2        V22 
#>  0.3307942  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V24        V25        V26         V3        V30        V32        V35 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V41        V42        V43        V46        V47        V50        V53 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V56        V58        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 
#>   0.173913