Regression Conditional Random Forest Learner

A random forest based on conditional inference trees (ctree). Calls partykit::cforest() from partykit.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.cforest")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		\([1, \infty)\)
replace	logical	FALSE	TRUE, FALSE	-
fraction	numeric	0.632		\([0, 1]\)
mtry	integer	-		\([0, \infty)\)
mtryratio	numeric	-		\([0, 1]\)
applyfun	untyped	-		-
cores	integer	NULL		\((-\infty, \infty)\)
trace	logical	FALSE	TRUE, FALSE	-
offset	untyped	-		-
cluster	untyped	-		-
scores	untyped	-		-
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Univariate	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
pargs	untyped	-		-
alpha	numeric	0.05		\([0, 1]\)
mincriterion	numeric	0		\([0, 1]\)
logmincriterion	numeric	0		\((-\infty, \infty)\)
minsplit	integer	20		\([1, \infty)\)
minbucket	integer	7		\([1, \infty)\)
minprob	numeric	0.01		\([0, 1]\)
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
maxvar	integer	-		\([1, \infty)\)
nresample	integer	9999		\([1, \infty)\)
tol	numeric	1.490116e-08		\([0, \infty)\)
maxsurrogate	integer	0		\([0, \infty)\)
numsurrogate	logical	FALSE	TRUE, FALSE	-
maxdepth	integer	Inf		\([0, \infty)\)
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		\([0, \infty)\)
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
saveinfo	logical	FALSE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
OOB	logical	FALSE	TRUE, FALSE	-
simplify	logical	TRUE	TRUE, FALSE	-
scale	logical	TRUE	TRUE, FALSE	-
nperm	integer	1		\([0, \infty)\)
risk	character	loglik	loglik, misclassification	-
conditional	logical	FALSE	TRUE, FALSE	-
threshold	numeric	0.2		\((-\infty, \infty)\)

Custom mlr3 parameters

• mtry:

  • This hyperparameter can alternatively be set via the added hyperparameter mtryratio as mtry = max(ceiling(mtryratio * n_features), 1). Note that mtry and mtryratio are mutually exclusive.

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

• Dictionary of Learners: mlr3::mlr_learners.

• 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.

Author

sumny

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCForest

Methods

Public methods

• LearnerRegrCForest$new()

• LearnerRegrCForest$oob_error()

• LearnerRegrCForest$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrCForest$new()

---

Method oob_error()

The out-of-bag error, calculated using the OOB predictions from partykit.

Usage

LearnerRegrCForest$oob_error()

Returns

numeric(1).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrCForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("regr.cforest")
print(learner)
#> <LearnerRegrCForest:regr.cforest>: Conditional Random Forest
#> * Model: -
#> * Parameters: teststat=quadratic, testtype=Univariate, mincriterion=0,
#>   saveinfo=FALSE
#> * Packages: mlr3, mlr3extralearners, partykit, sandwich, coin
#> * Predict Types:  [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: oob_error, weights

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
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#> [1] root * 
#> 
#> $nodes[[287]]
#> [1] root * 
#> 
#> $nodes[[288]]
#> [1] root * 
#> 
#> $nodes[[289]]
#> [1] root * 
#> 
#> $nodes[[290]]
#> [1] root * 
#> 
#> $nodes[[291]]
#> [1] root * 
#> 
#> $nodes[[292]]
#> [1] root * 
#> 
#> $nodes[[293]]
#> [1] root * 
#> 
#> $nodes[[294]]
#> [1] root * 
#> 
#> $nodes[[295]]
#> [1] root * 
#> 
#> $nodes[[296]]
#> [1] root * 
#> 
#> $nodes[[297]]
#> [1] root * 
#> 
#> $nodes[[298]]
#> [1] root * 
#> 
#> $nodes[[299]]
#> [1] root * 
#> 
#> $nodes[[300]]
#> [1] root * 
#> 
#> $nodes[[301]]
#> [1] root * 
#> 
#> $nodes[[302]]
#> [1] root * 
#> 
#> $nodes[[303]]
#> [1] root * 
#> 
#> $nodes[[304]]
#> [1] root * 
#> 
#> $nodes[[305]]
#> [1] root * 
#> 
#> $nodes[[306]]
#> [1] root * 
#> 
#> $nodes[[307]]
#> [1] root * 
#> 
#> $nodes[[308]]
#> [1] root * 
#> 
#> $nodes[[309]]
#> [1] root * 
#> 
#> $nodes[[310]]
#> [1] root * 
#> 
#> $nodes[[311]]
#> [1] root * 
#> 
#> $nodes[[312]]
#> [1] root * 
#> 
#> $nodes[[313]]
#> [1] root * 
#> 
#> $nodes[[314]]
#> [1] root * 
#> 
#> $nodes[[315]]
#> [1] root * 
#> 
#> $nodes[[316]]
#> [1] root * 
#> 
#> $nodes[[317]]
#> [1] root * 
#> 
#> $nodes[[318]]
#> [1] root * 
#> 
#> $nodes[[319]]
#> [1] root * 
#> 
#> $nodes[[320]]
#> [1] root * 
#> 
#> $nodes[[321]]
#> [1] root * 
#> 
#> $nodes[[322]]
#> [1] root * 
#> 
#> $nodes[[323]]
#> [1] root * 
#> 
#> $nodes[[324]]
#> [1] root * 
#> 
#> $nodes[[325]]
#> [1] root * 
#> 
#> $nodes[[326]]
#> [1] root * 
#> 
#> $nodes[[327]]
#> [1] root * 
#> 
#> $nodes[[328]]
#> [1] root * 
#> 
#> $nodes[[329]]
#> [1] root * 
#> 
#> $nodes[[330]]
#> [1] root * 
#> 
#> $nodes[[331]]
#> [1] root * 
#> 
#> $nodes[[332]]
#> [1] root * 
#> 
#> $nodes[[333]]
#> [1] root * 
#> 
#> $nodes[[334]]
#> [1] root * 
#> 
#> $nodes[[335]]
#> [1] root * 
#> 
#> $nodes[[336]]
#> [1] root * 
#> 
#> $nodes[[337]]
#> [1] root * 
#> 
#> $nodes[[338]]
#> [1] root * 
#> 
#> $nodes[[339]]
#> [1] root * 
#> 
#> $nodes[[340]]
#> [1] root * 
#> 
#> $nodes[[341]]
#> [1] root * 
#> 
#> $nodes[[342]]
#> [1] root * 
#> 
#> $nodes[[343]]
#> [1] root * 
#> 
#> $nodes[[344]]
#> [1] root * 
#> 
#> $nodes[[345]]
#> [1] root * 
#> 
#> $nodes[[346]]
#> [1] root * 
#> 
#> $nodes[[347]]
#> [1] root * 
#> 
#> $nodes[[348]]
#> [1] root * 
#> 
#> $nodes[[349]]
#> [1] root * 
#> 
#> $nodes[[350]]
#> [1] root * 
#> 
#> $nodes[[351]]
#> [1] root * 
#> 
#> $nodes[[352]]
#> [1] root * 
#> 
#> $nodes[[353]]
#> [1] root * 
#> 
#> $nodes[[354]]
#> [1] root * 
#> 
#> $nodes[[355]]
#> [1] root * 
#> 
#> $nodes[[356]]
#> [1] root * 
#> 
#> $nodes[[357]]
#> [1] root * 
#> 
#> $nodes[[358]]
#> [1] root * 
#> 
#> $nodes[[359]]
#> [1] root * 
#> 
#> $nodes[[360]]
#> [1] root * 
#> 
#> $nodes[[361]]
#> [1] root * 
#> 
#> $nodes[[362]]
#> [1] root * 
#> 
#> $nodes[[363]]
#> [1] root * 
#> 
#> $nodes[[364]]
#> [1] root * 
#> 
#> $nodes[[365]]
#> [1] root * 
#> 
#> $nodes[[366]]
#> [1] root * 
#> 
#> $nodes[[367]]
#> [1] root * 
#> 
#> $nodes[[368]]
#> [1] root * 
#> 
#> $nodes[[369]]
#> [1] root * 
#> 
#> $nodes[[370]]
#> [1] root * 
#> 
#> $nodes[[371]]
#> [1] root * 
#> 
#> $nodes[[372]]
#> [1] root * 
#> 
#> $nodes[[373]]
#> [1] root * 
#> 
#> $nodes[[374]]
#> [1] root * 
#> 
#> $nodes[[375]]
#> [1] root * 
#> 
#> $nodes[[376]]
#> [1] root * 
#> 
#> $nodes[[377]]
#> [1] root * 
#> 
#> $nodes[[378]]
#> [1] root * 
#> 
#> $nodes[[379]]
#> [1] root * 
#> 
#> $nodes[[380]]
#> [1] root * 
#> 
#> $nodes[[381]]
#> [1] root * 
#> 
#> $nodes[[382]]
#> [1] root * 
#> 
#> $nodes[[383]]
#> [1] root * 
#> 
#> $nodes[[384]]
#> [1] root * 
#> 
#> $nodes[[385]]
#> [1] root * 
#> 
#> $nodes[[386]]
#> [1] root * 
#> 
#> $nodes[[387]]
#> [1] root * 
#> 
#> $nodes[[388]]
#> [1] root * 
#> 
#> $nodes[[389]]
#> [1] root * 
#> 
#> $nodes[[390]]
#> [1] root * 
#> 
#> $nodes[[391]]
#> [1] root * 
#> 
#> $nodes[[392]]
#> [1] root * 
#> 
#> $nodes[[393]]
#> [1] root * 
#> 
#> $nodes[[394]]
#> [1] root * 
#> 
#> $nodes[[395]]
#> [1] root * 
#> 
#> $nodes[[396]]
#> [1] root * 
#> 
#> $nodes[[397]]
#> [1] root * 
#> 
#> $nodes[[398]]
#> [1] root * 
#> 
#> $nodes[[399]]
#> [1] root * 
#> 
#> $nodes[[400]]
#> [1] root * 
#> 
#> $nodes[[401]]
#> [1] root * 
#> 
#> $nodes[[402]]
#> [1] root * 
#> 
#> $nodes[[403]]
#> [1] root * 
#> 
#> $nodes[[404]]
#> [1] root * 
#> 
#> $nodes[[405]]
#> [1] root * 
#> 
#> $nodes[[406]]
#> [1] root * 
#> 
#> $nodes[[407]]
#> [1] root * 
#> 
#> $nodes[[408]]
#> [1] root * 
#> 
#> $nodes[[409]]
#> [1] root * 
#> 
#> $nodes[[410]]
#> [1] root * 
#> 
#> $nodes[[411]]
#> [1] root * 
#> 
#> $nodes[[412]]
#> [1] root * 
#> 
#> $nodes[[413]]
#> [1] root * 
#> 
#> $nodes[[414]]
#> [1] root * 
#> 
#> $nodes[[415]]
#> [1] root * 
#> 
#> $nodes[[416]]
#> [1] root * 
#> 
#> $nodes[[417]]
#> [1] root * 
#> 
#> $nodes[[418]]
#> [1] root * 
#> 
#> $nodes[[419]]
#> [1] root * 
#> 
#> $nodes[[420]]
#> [1] root * 
#> 
#> $nodes[[421]]
#> [1] root * 
#> 
#> $nodes[[422]]
#> [1] root * 
#> 
#> $nodes[[423]]
#> [1] root * 
#> 
#> $nodes[[424]]
#> [1] root * 
#> 
#> $nodes[[425]]
#> [1] root * 
#> 
#> $nodes[[426]]
#> [1] root * 
#> 
#> $nodes[[427]]
#> [1] root * 
#> 
#> $nodes[[428]]
#> [1] root * 
#> 
#> $nodes[[429]]
#> [1] root * 
#> 
#> $nodes[[430]]
#> [1] root * 
#> 
#> $nodes[[431]]
#> [1] root * 
#> 
#> $nodes[[432]]
#> [1] root * 
#> 
#> $nodes[[433]]
#> [1] root * 
#> 
#> $nodes[[434]]
#> [1] root * 
#> 
#> $nodes[[435]]
#> [1] root * 
#> 
#> $nodes[[436]]
#> [1] root * 
#> 
#> $nodes[[437]]
#> [1] root * 
#> 
#> $nodes[[438]]
#> [1] root * 
#> 
#> $nodes[[439]]
#> [1] root * 
#> 
#> $nodes[[440]]
#> [1] root * 
#> 
#> $nodes[[441]]
#> [1] root * 
#> 
#> $nodes[[442]]
#> [1] root * 
#> 
#> $nodes[[443]]
#> [1] root * 
#> 
#> $nodes[[444]]
#> [1] root * 
#> 
#> $nodes[[445]]
#> [1] root * 
#> 
#> $nodes[[446]]
#> [1] root * 
#> 
#> $nodes[[447]]
#> [1] root * 
#> 
#> $nodes[[448]]
#> [1] root * 
#> 
#> $nodes[[449]]
#> [1] root * 
#> 
#> $nodes[[450]]
#> [1] root * 
#> 
#> $nodes[[451]]
#> [1] root * 
#> 
#> $nodes[[452]]
#> [1] root * 
#> 
#> $nodes[[453]]
#> [1] root * 
#> 
#> $nodes[[454]]
#> [1] root * 
#> 
#> $nodes[[455]]
#> [1] root * 
#> 
#> $nodes[[456]]
#> [1] root * 
#> 
#> $nodes[[457]]
#> [1] root * 
#> 
#> $nodes[[458]]
#> [1] root * 
#> 
#> $nodes[[459]]
#> [1] root * 
#> 
#> $nodes[[460]]
#> [1] root * 
#> 
#> $nodes[[461]]
#> [1] root * 
#> 
#> $nodes[[462]]
#> [1] root * 
#> 
#> $nodes[[463]]
#> [1] root * 
#> 
#> $nodes[[464]]
#> [1] root * 
#> 
#> $nodes[[465]]
#> [1] root * 
#> 
#> $nodes[[466]]
#> [1] root * 
#> 
#> $nodes[[467]]
#> [1] root * 
#> 
#> $nodes[[468]]
#> [1] root * 
#> 
#> $nodes[[469]]
#> [1] root * 
#> 
#> $nodes[[470]]
#> [1] root * 
#> 
#> $nodes[[471]]
#> [1] root * 
#> 
#> $nodes[[472]]
#> [1] root * 
#> 
#> $nodes[[473]]
#> [1] root * 
#> 
#> $nodes[[474]]
#> [1] root * 
#> 
#> $nodes[[475]]
#> [1] root * 
#> 
#> $nodes[[476]]
#> [1] root * 
#> 
#> $nodes[[477]]
#> [1] root * 
#> 
#> $nodes[[478]]
#> [1] root * 
#> 
#> $nodes[[479]]
#> [1] root * 
#> 
#> $nodes[[480]]
#> [1] root * 
#> 
#> $nodes[[481]]
#> [1] root * 
#> 
#> $nodes[[482]]
#> [1] root * 
#> 
#> $nodes[[483]]
#> [1] root * 
#> 
#> $nodes[[484]]
#> [1] root * 
#> 
#> $nodes[[485]]
#> [1] root * 
#> 
#> $nodes[[486]]
#> [1] root * 
#> 
#> $nodes[[487]]
#> [1] root * 
#> 
#> $nodes[[488]]
#> [1] root * 
#> 
#> $nodes[[489]]
#> [1] root * 
#> 
#> $nodes[[490]]
#> [1] root * 
#> 
#> $nodes[[491]]
#> [1] root * 
#> 
#> $nodes[[492]]
#> [1] root * 
#> 
#> $nodes[[493]]
#> [1] root * 
#> 
#> $nodes[[494]]
#> [1] root * 
#> 
#> $nodes[[495]]
#> [1] root * 
#> 
#> $nodes[[496]]
#> [1] root * 
#> 
#> $nodes[[497]]
#> [1] root * 
#> 
#> $nodes[[498]]
#> [1] root * 
#> 
#> $nodes[[499]]
#> [1] root * 
#> 
#> $nodes[[500]]
#> [1] root * 
#> 
#> 
#> $data
#>     mpg am carb cyl  disp drat gear  hp  qsec vs    wt
#> 1  21.0  1    4   6 160.0 3.90    4 110 16.46  0 2.620
#> 2  21.4  0    1   6 258.0 3.08    3 110 19.44  1 3.215
#> 3  18.1  0    1   6 225.0 2.76    3 105 20.22  1 3.460
#> 4  14.3  0    4   8 360.0 3.21    3 245 15.84  0 3.570
#> 5  24.4  0    2   4 146.7 3.69    4  62 20.00  1 3.190
#> 6  16.4  0    3   8 275.8 3.07    3 180 17.40  0 4.070
#> 7  17.3  0    3   8 275.8 3.07    3 180 17.60  0 3.730
#> 8  10.4  0    4   8 460.0 3.00    3 215 17.82  0 5.424
#> 9  14.7  0    4   8 440.0 3.23    3 230 17.42  0 5.345
#> 10 21.5  0    1   4 120.1 3.70    3  97 20.01  1 2.465
#> 11 15.5  0    2   8 318.0 2.76    3 150 16.87  0 3.520
#> 12 15.2  0    2   8 304.0 3.15    3 150 17.30  0 3.435
#> 13 13.3  0    4   8 350.0 3.73    3 245 15.41  0 3.840
#> 14 19.2  0    2   8 400.0 3.08    3 175 17.05  0 3.845
#> 15 27.3  1    1   4  79.0 4.08    4  66 18.90  1 1.935
#> 16 26.0  1    2   4 120.3 4.43    5  91 16.70  0 2.140
#> 17 30.4  1    2   4  95.1 3.77    5 113 16.90  1 1.513
#> 18 15.8  1    4   8 351.0 4.22    5 264 14.50  0 3.170
#> 19 19.7  1    6   6 145.0 3.62    5 175 15.50  0 2.770
#> 20 15.0  1    8   8 301.0 3.54    5 335 14.60  0 3.570
#> 21 21.4  1    2   4 121.0 4.11    4 109 18.60  1 2.780
#> 
#> $weights
#> $weights[[1]]
#>  [1] 0 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 1 0 1
#> 
#> $weights[[2]]
#>  [1] 1 0 1 1 1 1 0 1 1 0 0 1 1 0 0 1 0 1 0 1 1
#> 
#> $weights[[3]]
#>  [1] 1 1 0 0 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0 0 0
#> 
#> $weights[[4]]
#>  [1] 0 1 1 1 1 0 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1
#> 
#> $weights[[5]]
#>  [1] 0 1 0 0 1 1 1 1 0 0 0 1 1 1 0 1 1 1 1 0 1
#> 
#> $weights[[6]]
#>  [1] 0 1 1 1 0 1 1 1 1 0 1 0 0 0 0 0 1 1 1 1 1
#> 
#> $weights[[7]]
#>  [1] 0 1 1 0 0 1 0 1 1 1 0 0 1 1 1 0 1 0 1 1 1
#> 
#> $weights[[8]]
#>  [1] 0 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 0 1 1 1 1
#> 
#> $weights[[9]]
#>  [1] 1 0 1 0 1 0 0 1 1 1 1 1 0 1 0 1 1 1 0 0 1
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#> $weights[[10]]
#>  [1] 1 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 0 1 0 0
#> 
#> $weights[[11]]
#>  [1] 0 1 0 0 1 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0
#> 
#> $weights[[12]]
#>  [1] 0 1 0 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 1 1 0
#> 
#> $weights[[13]]
#>  [1] 0 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 0 1 1 1 1
#> 
#> $weights[[14]]
#>  [1] 1 0 0 0 0 1 0 1 1 1 1 1 1 1 1 0 1 0 1 0 1
#> 
#> $weights[[15]]
#>  [1] 0 1 0 1 1 0 1 1 1 0 1 0 1 1 1 1 0 1 0 1 0
#> 
#> $weights[[16]]
#>  [1] 0 1 1 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 1
#> 
#> $weights[[17]]
#>  [1] 0 0 1 1 0 1 1 1 1 1 0 1 0 1 0 0 1 1 0 1 1
#> 
#> $weights[[18]]
#>  [1] 1 0 1 1 1 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0
#> 
#> $weights[[19]]
#>  [1] 0 1 0 1 1 0 0 1 0 0 1 1 1 0 1 1 0 1 1 1 1
#> 
#> $weights[[20]]
#>  [1] 1 0 0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 1 0 1 1
#> 
#> $weights[[21]]
#>  [1] 0 1 1 0 0 1 0 0 1 0 1 1 1 1 1 1 0 1 0 1 1
#> 
#> $weights[[22]]
#>  [1] 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 0 0
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#> $weights[[23]]
#>  [1] 0 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 0 1 1 0 1
#> 
#> $weights[[24]]
#>  [1] 0 1 0 1 0 1 1 0 0 0 1 1 1 0 0 1 1 1 1 1 1
#> 
#> $weights[[25]]
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#> 
#> $weights[[26]]
#>  [1] 1 1 1 0 0 0 1 1 0 1 0 1 1 1 0 1 1 0 1 1 0
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#> $weights[[27]]
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#> $weights[[28]]
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#> $weights[[45]]
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#> $weights[[46]]
#>  [1] 1 1 1 1 0 0 1 1 0 1 1 1 0 1 0 0 1 1 0 0 1
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#> $weights[[47]]
#>  [1] 0 1 1 1 1 1 1 1 0 0 1 1 0 0 1 1 0 1 1 0 0
#> 
#> $weights[[48]]
#>  [1] 1 1 0 1 1 0 1 0 1 0 0 1 0 1 1 1 0 1 1 0 1
#> 
#> $weights[[49]]
#>  [1] 0 1 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1 1 0 1
#> 
#> $weights[[50]]
#>  [1] 0 1 0 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1
#> 
#> $weights[[51]]
#>  [1] 0 0 1 0 1 1 1 1 1 0 1 0 0 0 1 0 1 1 1 1 1
#> 
#> $weights[[52]]
#>  [1] 0 1 1 0 1 0 0 1 1 0 1 1 1 1 0 1 0 1 0 1 1
#> 
#> $weights[[53]]
#>  [1] 1 1 1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 0 0 0 1
#> 
#> $weights[[54]]
#>  [1] 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 0
#> 
#> $weights[[55]]
#>  [1] 1 0 0 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 0 1
#> 
#> $weights[[56]]
#>  [1] 1 0 1 1 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 1 1
#> 
#> $weights[[57]]
#>  [1] 1 1 0 1 1 0 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1
#> 
#> $weights[[58]]
#>  [1] 1 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 0
#> 
#> $weights[[59]]
#>  [1] 1 0 0 0 0 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1
#> 
#> $weights[[60]]
#>  [1] 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1 1
#> 
#> $weights[[61]]
#>  [1] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 1 1 1 0 0 0 1
#> 
#> $weights[[62]]
#>  [1] 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 0 0
#> 
#> $weights[[63]]
#>  [1] 1 0 1 0 1 0 0 1 1 0 0 0 0 1 1 1 1 1 1 1 1
#> 
#> $weights[[64]]
#>  [1] 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 1 0 1 0 1 0
#> 
#> $weights[[65]]
#>  [1] 0 0 1 1 1 0 1 0 0 1 1 1 0 1 1 0 1 0 1 1 1
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#>  [1] 1 0 1 1 1 0 1 0 0 1 0 1 1 0 1 1 1 0 0 1 1
#> 
#> $weights[[455]]
#>  [1] 0 1 1 1 0 1 0 1 0 1 1 1 1 0 0 1 0 1 1 0 1
#> 
#> $weights[[456]]
#>  [1] 1 1 1 1 0 1 1 0 1 0 1 0 0 1 0 0 1 1 1 0 1
#> 
#> $weights[[457]]
#>  [1] 0 1 1 1 0 1 1 0 1 1 0 0 1 0 1 1 0 0 1 1 1
#> 
#> $weights[[458]]
#>  [1] 1 0 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 1 0 0
#> 
#> $weights[[459]]
#>  [1] 0 0 0 1 0 1 0 1 1 0 0 1 1 1 1 1 0 1 1 1 1
#> 
#> $weights[[460]]
#>  [1] 1 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0 1 0 0
#> 
#> $weights[[461]]
#>  [1] 0 0 1 0 0 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 0
#> 
#> $weights[[462]]
#>  [1] 0 0 1 1 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1 0 1
#> 
#> $weights[[463]]
#>  [1] 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 1 1
#> 
#> $weights[[464]]
#>  [1] 1 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 1 1 1 0 0
#> 
#> $weights[[465]]
#>  [1] 1 1 1 1 0 0 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1
#> 
#> $weights[[466]]
#>  [1] 1 1 0 1 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 1 0
#> 
#> $weights[[467]]
#>  [1] 0 1 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 0
#> 
#> $weights[[468]]
#>  [1] 0 0 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 0 1
#> 
#> $weights[[469]]
#>  [1] 1 1 1 1 0 1 1 1 0 0 0 1 1 1 0 1 1 0 0 1 0
#> 
#> $weights[[470]]
#>  [1] 0 1 0 1 1 1 1 1 0 1 1 0 0 0 1 1 1 1 0 0 1
#> 
#> $weights[[471]]
#>  [1] 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 0 1 0 0
#> 
#> $weights[[472]]
#>  [1] 1 1 1 0 0 0 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1
#> 
#> $weights[[473]]
#>  [1] 1 1 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1
#> 
#> $weights[[474]]
#>  [1] 0 0 1 1 1 0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
#> 
#> $weights[[475]]
#>  [1] 0 1 0 0 0 1 1 1 1 1 0 1 0 1 1 1 1 1 0 0 1
#> 
#> $weights[[476]]
#>  [1] 0 0 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 1 0
#> 
#> $weights[[477]]
#>  [1] 1 0 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 0 1 1 1
#> 
#> $weights[[478]]
#>  [1] 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0 0 1
#> 
#> $weights[[479]]
#>  [1] 0 0 1 1 0 1 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1
#> 
#> $weights[[480]]
#>  [1] 1 1 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 0 1 1 0
#> 
#> $weights[[481]]
#>  [1] 0 1 0 0 1 1 1 1 1 1 0 0 1 0 0 0 1 1 1 1 1
#> 
#> $weights[[482]]
#>  [1] 1 0 1 1 0 0 0 0 1 1 1 0 1 0 1 1 1 1 1 0 1
#> 
#> $weights[[483]]
#>  [1] 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1
#> 
#> $weights[[484]]
#>  [1] 0 1 1 1 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 1 1
#> 
#> $weights[[485]]
#>  [1] 1 1 1 0 1 1 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1
#> 
#> $weights[[486]]
#>  [1] 1 0 0 1 1 1 0 1 1 1 1 0 0 1 1 0 0 1 1 1 0
#> 
#> $weights[[487]]
#>  [1] 1 0 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
#> 
#> $weights[[488]]
#>  [1] 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1 0 0 1 1 0 1
#> 
#> $weights[[489]]
#>  [1] 1 0 1 1 0 0 1 0 1 0 1 1 1 1 1 0 0 1 1 0 1
#> 
#> $weights[[490]]
#>  [1] 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0 1 0 1 1 1 1
#> 
#> $weights[[491]]
#>  [1] 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1
#> 
#> $weights[[492]]
#>  [1] 1 1 0 0 1 0 0 0 1 1 1 0 1 1 1 1 0 0 1 1 1
#> 
#> $weights[[493]]
#>  [1] 1 1 0 1 1 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 1
#> 
#> $weights[[494]]
#>  [1] 0 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 1
#> 
#> $weights[[495]]
#>  [1] 1 0 1 0 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 0 0
#> 
#> $weights[[496]]
#>  [1] 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1 0 1
#> 
#> $weights[[497]]
#>  [1] 0 1 1 1 1 1 1 0 0 1 0 0 0 0 1 1 0 1 1 1 1
#> 
#> $weights[[498]]
#>  [1] 0 1 1 1 1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 0 0
#> 
#> $weights[[499]]
#>  [1] 1 1 1 0 0 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1
#> 
#> $weights[[500]]
#>  [1] 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 0 0 1 0
#> 
#> 
#> $fitted
#>    idx (response)
#> 1    1       21.0
#> 2    2       21.4
#> 3    3       18.1
#> 4    4       14.3
#> 5    5       24.4
#> 6    6       16.4
#> 7    7       17.3
#> 8    8       10.4
#> 9    9       14.7
#> 10  10       21.5
#> 11  11       15.5
#> 12  12       15.2
#> 13  13       13.3
#> 14  14       19.2
#> 15  15       27.3
#> 16  16       26.0
#> 17  17       30.4
#> 18  18       15.8
#> 19  19       19.7
#> 20  20       15.0
#> 21  21       21.4
#> 
#> $terms
#> mpg ~ am + carb + cyl + disp + drat + gear + hp + qsec + vs + 
#>     wt
#> attr(,"variables")
#> list(mpg, am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)
#> attr(,"factors")
#>      am carb cyl disp drat gear hp qsec vs wt
#> mpg   0    0   0    0    0    0  0    0  0  0
#> am    1    0   0    0    0    0  0    0  0  0
#> carb  0    1   0    0    0    0  0    0  0  0
#> cyl   0    0   1    0    0    0  0    0  0  0
#> disp  0    0   0    1    0    0  0    0  0  0
#> drat  0    0   0    0    1    0  0    0  0  0
#> gear  0    0   0    0    0    1  0    0  0  0
#> hp    0    0   0    0    0    0  1    0  0  0
#> qsec  0    0   0    0    0    0  0    1  0  0
#> vs    0    0   0    0    0    0  0    0  1  0
#> wt    0    0   0    0    0    0  0    0  0  1
#> attr(,"term.labels")
#>  [1] "am"   "carb" "cyl"  "disp" "drat" "gear" "hp"   "qsec" "vs"   "wt"  
#> attr(,"order")
#>  [1] 1 1 1 1 1 1 1 1 1 1
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 1
#> attr(,"Formula_with_dot")
#> mpg ~ .
#> NULL
#> attr(,"Formula_without_dot")
#> mpg ~ am + carb + cyl + disp + drat + gear + hp + qsec + vs + 
#>     wt
#> NULL
#> attr(,"dot")
#> [1] "sequential"
#> 
#> $info
#> $info$call
#> partykit::cforest(formula = task$formula(), data = task$data(), 
#>     weights = task$weights$weight, control = control, perturb = perturb)
#> 
#> $info$control
#> $info$control$criterion
#> [1] "p.value"
#> 
#> $info$control$logmincriterion
#> [1] -Inf
#> 
#> $info$control$minsplit
#> [1] 20
#> 
#> $info$control$minbucket
#> [1] 7
#> 
#> $info$control$minprob
#> [1] 0.01
#> 
#> $info$control$maxvar
#> [1] Inf
#> 
#> $info$control$stump
#> [1] FALSE
#> 
#> $info$control$nmax
#>  yx   z 
#> Inf Inf 
#> 
#> $info$control$lookahead
#> [1] FALSE
#> 
#> $info$control$mtry
#> [1] 4
#> 
#> $info$control$maxdepth
#> [1] Inf
#> 
#> $info$control$multiway
#> [1] FALSE
#> 
#> $info$control$splittry
#> [1] 2
#> 
#> $info$control$maxsurrogate
#> [1] 0
#> 
#> $info$control$numsurrogate
#> [1] FALSE
#> 
#> $info$control$majority
#> [1] FALSE
#> 
#> $info$control$caseweights
#> [1] TRUE
#> 
#> $info$control$applyfun
#> function (X, FUN, ...) 
#> {
#>     FUN <- match.fun(FUN)
#>     if (!is.vector(X) || is.object(X)) 
#>         X <- as.list(X)
#>     .Internal(lapply(X, FUN))
#> }
#> <bytecode: 0x55793eda5a30>
#> <environment: namespace:base>
#> 
#> $info$control$saveinfo
#> [1] FALSE
#> 
#> $info$control$bonferroni
#> [1] FALSE
#> 
#> $info$control$update
#> [1] FALSE
#> 
#> $info$control$selectfun
#> function (model, trafo, data, subset, weights, whichvar, ctrl) 
#> {
#>     args <- list(...)
#>     ctrl[names(args)] <- args
#>     .select(model, trafo, data, subset, weights, whichvar, ctrl, 
#>         FUN = .ctree_test)
#> }
#> <bytecode: 0x55794beac450>
#> <environment: 0x55795d2a6660>
#> 
#> $info$control$splitfun
#> function (model, trafo, data, subset, weights, whichvar, ctrl) 
#> {
#>     args <- list(...)
#>     ctrl[names(args)] <- args
#>     .split(model, trafo, data, subset, weights, whichvar, ctrl, 
#>         FUN = .ctree_test)
#> }
#> <bytecode: 0x55794beaa370>
#> <environment: 0x55795d2a6740>
#> 
#> $info$control$svselectfun
#> function (model, trafo, data, subset, weights, whichvar, ctrl) 
#> {
#>     args <- list(...)
#>     ctrl[names(args)] <- args
#>     .select(model, trafo, data, subset, weights, whichvar, ctrl, 
#>         FUN = .ctree_test)
#> }
#> <bytecode: 0x55794beac450>
#> <environment: 0x55795d2a6820>
#> 
#> $info$control$svsplitfun
#> function (model, trafo, data, subset, weights, whichvar, ctrl) 
#> {
#>     args <- list(...)
#>     ctrl[names(args)] <- args
#>     .split(model, trafo, data, subset, weights, whichvar, ctrl, 
#>         FUN = .ctree_test)
#> }
#> <bytecode: 0x55794beaa370>
#> <environment: 0x55795d2a6970>
#> 
#> $info$control$teststat
#> [1] "quadratic"
#> 
#> $info$control$splitstat
#> [1] "quadratic"
#> 
#> $info$control$splittest
#> [1] FALSE
#> 
#> $info$control$pargs
#> $maxpts
#> [1] 25000
#> 
#> $abseps
#> [1] 0.001
#> 
#> $releps
#> [1] 0
#> 
#> attr(,"class")
#> [1] "GenzBretz"
#> 
#> $info$control$testtype
#> [1] "Univariate"
#> 
#> $info$control$nresample
#> [1] 9999
#> 
#> $info$control$tol
#> [1] 1.490116e-08
#> 
#> $info$control$intersplit
#> [1] FALSE
#> 
#> $info$control$MIA
#> [1] FALSE
#> 
#> 
#> 
#> $trafo
#> function (subset, weights, info, estfun, object, ...) 
#> list(estfun = Y, unweighted = TRUE)
#> <bytecode: 0x55794be52a60>
#> <environment: 0x55795af1c548>
#> 
#> $predictf
#> ~am + carb + cyl + disp + drat + gear + hp + qsec + vs + wt
#> attr(,"variables")
#> list(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)
#> attr(,"factors")
#>      am carb cyl disp drat gear hp qsec vs wt
#> am    1    0   0    0    0    0  0    0  0  0
#> carb  0    1   0    0    0    0  0    0  0  0
#> cyl   0    0   1    0    0    0  0    0  0  0
#> disp  0    0   0    1    0    0  0    0  0  0
#> drat  0    0   0    0    1    0  0    0  0  0
#> gear  0    0   0    0    0    1  0    0  0  0
#> hp    0    0   0    0    0    0  1    0  0  0
#> qsec  0    0   0    0    0    0  0    1  0  0
#> vs    0    0   0    0    0    0  0    0  1  0
#> wt    0    0   0    0    0    0  0    0  0  1
#> attr(,"term.labels")
#>  [1] "am"   "carb" "cyl"  "disp" "drat" "gear" "hp"   "qsec" "vs"   "wt"  
#> attr(,"order")
#>  [1] 1 1 1 1 1 1 1 1 1 1
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 0
#> attr(,"Formula_with_dot")
#> mpg ~ .
#> NULL
#> attr(,"Formula_without_dot")
#> mpg ~ am + carb + cyl + disp + drat + gear + hp + qsec + vs + 
#>     wt
#> NULL
#> attr(,"dot")
#> [1] "sequential"
#> 
#> attr(,"class")
#> [1] "cforest"      "constparties" "parties"     


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> regr.mse 
#> 59.61003