Regression Model-based Recursive Partitioning Learner
mlr_learners_regr.mob.RdModel-based recursive partitioning algorithm.
Calls partykit::mob() from mob.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():
Meta Information
Task type: “regr”
Predict Types: “response”, “se”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin
Parameters
| Id | Type | Default | Levels | Range |
| rhs | untyped | - | - | |
| fit | untyped | - | - | |
| offset | untyped | - | - | |
| cluster | untyped | - | - | |
| alpha | numeric | 0.05 | \([0, 1]\) | |
| bonferroni | logical | TRUE | TRUE, FALSE | - |
| minsize | integer | - | \([1, \infty)\) | |
| minsplit | integer | - | \([1, \infty)\) | |
| minbucket | integer | - | \([1, \infty)\) | |
| maxdepth | integer | Inf | \([0, \infty)\) | |
| mtry | integer | Inf | \([0, \infty)\) | |
| trim | numeric | 0.1 | \([0, \infty)\) | |
| breakties | logical | FALSE | TRUE, FALSE | - |
| parm | untyped | - | - | |
| dfsplit | integer | - | \([0, \infty)\) | |
| prune | untyped | - | - | |
| restart | logical | TRUE | TRUE, FALSE | - |
| verbose | logical | FALSE | TRUE, FALSE | - |
| caseweights | logical | TRUE | TRUE, FALSE | - |
| ytype | character | vector | vector, matrix, data.frame | - |
| xtype | character | matrix | vector, matrix, data.frame | - |
| terminal | untyped | "object" | - | |
| inner | untyped | "object" | - | |
| model | logical | TRUE | TRUE, FALSE | - |
| numsplit | character | left | left, center | - |
| catsplit | character | binary | binary, multiway | - |
| vcov | character | opg | opg, info, sandwich | - |
| ordinal | character | chisq | chisq, max, L2 | - |
| nrep | integer | 10000 | \([0, \infty)\) | |
| applyfun | untyped | - | - | |
| cores | integer | NULL | \((-\infty, \infty)\) | |
| additional | untyped | - | - | |
| predict_fun | untyped | - | - |
References
Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.
Hothorn T, Hornik K, Zeileis A (2006). “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics, 15(3), 651--674. doi:10.1198/106186006x133933 , https://doi.org/10.1198/106186006x133933.
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::LearnerRegr -> LearnerRegrMob
Examples
library(mlr3)
lm_ = function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
lm(y ~ 1, ...)
}
learner = LearnerRegrMob$new()
learner$param_set$values$rhs = "."
learner$param_set$values$fit = lm_
learner$feature_types = c("logical", "integer", "numeric", "factor", "ordered")
predict_fun = function(object, newdata, task, .type) {
preds = predict(object, newdata = newdata, type = "response", se.fit = TRUE)
cbind(preds$fit, preds$se.fit)
}
learner$param_set$values$predict_fun = predict_fun
task = tsk("mtcars")
ids = partition(task)
learner$train(task, row_ids = ids$train)
learner$predict(task, row_ids = ids$test)
#> <PredictionRegr> for 11 observations:
#> row_ids truth response
#> 1 21.0 24.83000
#> 5 18.7 15.03636
#> 9 22.8 24.83000
#> ---
#> 29 15.8 24.83000
#> 18 32.4 24.83000
#> 28 30.4 24.83000