Regression Conditional Random Forest Learner
mlr_learners_regr.cforest.Rd
A random forest based on conditional inference trees (ctree).
Calls partykit::cforest()
from partykit.
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
asmtry = max(ceiling(mtryratio * n_features), 1)
. Note thatmtry
andmtryratio
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
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
-> LearnerRegrCForest
Methods
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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#>
#> $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 21.0 1 4 6 160.0 3.90 4 110 17.02 0 2.875
#> 3 22.8 1 1 4 108.0 3.85 4 93 18.61 1 2.320
#> 4 21.4 0 1 6 258.0 3.08 3 110 19.44 1 3.215
#> 5 18.1 0 1 6 225.0 2.76 3 105 20.22 1 3.460
#> 6 22.8 0 2 4 140.8 3.92 4 95 22.90 1 3.150
#> 7 19.2 0 4 6 167.6 3.92 4 123 18.30 1 3.440
#> 8 16.4 0 3 8 275.8 3.07 3 180 17.40 0 4.070
#> 9 17.3 0 3 8 275.8 3.07 3 180 17.60 0 3.730
#> 10 15.2 0 3 8 275.8 3.07 3 180 18.00 0 3.780
#> 11 10.4 0 4 8 472.0 2.93 3 205 17.98 0 5.250
#> 12 10.4 0 4 8 460.0 3.00 3 215 17.82 0 5.424
#> 13 32.4 1 1 4 78.7 4.08 4 66 19.47 1 2.200
#> 14 33.9 1 1 4 71.1 4.22 4 65 19.90 1 1.835
#> 15 15.5 0 2 8 318.0 2.76 3 150 16.87 0 3.520
#> 16 15.2 0 2 8 304.0 3.15 3 150 17.30 0 3.435
#> 17 13.3 0 4 8 350.0 3.73 3 245 15.41 0 3.840
#> 18 27.3 1 1 4 79.0 4.08 4 66 18.90 1 1.935
#> 19 26.0 1 2 4 120.3 4.43 5 91 16.70 0 2.140
#> 20 19.7 1 6 6 145.0 3.62 5 175 15.50 0 2.770
#> 21 21.4 1 2 4 121.0 4.11 4 109 18.60 1 2.780
#>
#> $weights
#> $weights[[1]]
#> [1] 1 0 1 0 0 1 1 1 1 1 0 1 0 0 0 1 0 1 1 1 1
#>
#> $weights[[2]]
#> [1] 1 1 0 1 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1 1 1
#>
#> $weights[[3]]
#> [1] 1 0 1 0 0 1 1 1 0 1 1 1 1 0 0 0 1 0 1 1 1
#>
#> $weights[[4]]
#> [1] 0 1 1 1 1 0 1 1 0 1 1 1 1 0 0 0 1 1 1 0 0
#>
#> $weights[[5]]
#> [1] 0 1 1 1 0 1 0 1 1 1 1 1 0 0 1 1 1 0 1 0 0
#>
#> $weights[[6]]
#> [1] 1 1 1 1 0 0 1 0 1 1 1 0 1 0 0 1 1 0 1 1 0
#>
#> $weights[[7]]
#> [1] 0 1 1 0 1 0 0 1 1 0 1 1 1 0 1 0 1 1 1 1 0
#>
#> $weights[[8]]
#> [1] 1 0 1 1 0 0 1 1 0 1 0 1 1 1 0 0 1 0 1 1 1
#>
#> $weights[[9]]
#> [1] 1 1 0 1 1 1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
#>
#> $weights[[10]]
#> [1] 1 0 1 1 1 1 0 0 1 0 0 0 0 1 1 1 0 1 1 1 1
#>
#> $weights[[11]]
#> [1] 0 1 1 1 0 0 1 1 1 0 1 0 0 1 1 0 1 1 1 1 0
#>
#> $weights[[12]]
#> [1] 1 0 1 1 0 0 0 1 0 0 1 1 1 1 1 1 0 1 1 0 1
#>
#> $weights[[13]]
#> [1] 0 1 1 1 0 1 1 0 1 1 0 0 0 1 1 1 1 1 0 0 1
#>
#> $weights[[14]]
#> [1] 1 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 0 1 0 1 0
#>
#> $weights[[15]]
#> [1] 1 1 0 1 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 0 1
#>
#> $weights[[16]]
#> [1] 1 1 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
#>
#> $weights[[17]]
#> [1] 0 1 0 1 1 1 0 0 1 1 1 1 0 0 1 1 0 1 0 1 1
#>
#> $weights[[18]]
#> [1] 1 0 0 1 0 1 1 1 0 0 1 1 1 0 1 1 1 0 0 1 1
#>
#> $weights[[19]]
#> [1] 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 0 0 0 1 1 1
#>
#> $weights[[20]]
#> [1] 1 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 0 0 0
#>
#> $weights[[21]]
#> [1] 0 1 1 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 1
#>
#> $weights[[22]]
#> [1] 1 1 0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 1 1 0 1
#>
#> $weights[[23]]
#> [1] 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 1 0 1 1 0 1
#>
#> $weights[[24]]
#> [1] 0 1 1 0 0 0 1 1 1 1 0 1 1 0 1 1 1 0 0 1 1
#>
#> $weights[[25]]
#> [1] 1 1 0 1 0 1 1 1 0 0 1 1 1 0 0 1 0 1 1 0 1
#>
#> $weights[[26]]
#> [1] 0 0 0 1 1 0 1 0 1 0 1 1 1 0 1 1 1 0 1 1 1
#>
#> $weights[[27]]
#> [1] 1 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 0 0 1
#>
#> $weights[[28]]
#> [1] 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 0 0
#>
#> $weights[[29]]
#> [1] 1 1 1 0 1 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0
#>
#> $weights[[30]]
#> [1] 1 0 0 1 1 1 0 1 1 1 0 1 0 0 1 1 0 0 1 1 1
#>
#> $weights[[31]]
#> [1] 1 1 1 1 0 1 1 1 0 1 0 0 1 0 0 1 1 0 1 0 1
#>
#> $weights[[32]]
#> [1] 1 1 0 1 0 1 1 1 1 0 1 1 0 1 0 0 1 1 0 1 0
#>
#> $weights[[33]]
#> [1] 0 0 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 1 1 1
#>
#> $weights[[34]]
#> [1] 1 0 1 0 1 0 1 1 0 0 1 1 0 1 0 1 1 0 1 1 1
#>
#> $weights[[35]]
#> [1] 1 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 1 0 0 1 1
#>
#> $weights[[36]]
#> [1] 0 0 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0
#>
#> $weights[[37]]
#> [1] 1 0 1 1 0 1 0 1 1 0 1 1 1 0 1 0 1 0 1 0 1
#>
#> $weights[[38]]
#> [1] 0 1 0 1 1 0 0 1 1 1 0 1 0 1 1 1 0 0 1 1 1
#>
#> $weights[[39]]
#> [1] 1 0 1 0 0 1 1 0 0 1 1 0 1 1 0 1 1 1 1 1 0
#>
#> $weights[[40]]
#> [1] 1 1 0 1 1 1 1 1 0 1 0 1 1 0 1 0 1 0 0 1 0
#>
#> $weights[[41]]
#> [1] 0 1 1 1 1 0 0 0 1 1 1 1 0 1 1 0 1 1 1 0 0
#>
#> $weights[[42]]
#> [1] 1 0 1 0 1 0 1 1 0 1 0 0 1 1 1 0 0 1 1 1 1
#>
#> $weights[[43]]
#> [1] 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1 0 0 1 1 1
#>
#> $weights[[44]]
#> [1] 0 1 0 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 1
#>
#> $weights[[45]]
#> [1] 1 0 1 1 0 0 1 1 1 1 0 1 1 1 0 1 0 0 1 0 1
#>
#> $weights[[46]]
#> [1] 0 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0
#>
#> $weights[[47]]
#> [1] 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 0 1 1
#>
#> $weights[[48]]
#> [1] 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0
#>
#> $weights[[49]]
#> [1] 1 1 1 1 1 1 0 1 0 0 0 1 1 1 1 0 0 1 0 0 1
#>
#> $weights[[50]]
#> [1] 1 0 1 0 0 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 0
#>
#> $weights[[51]]
#> [1] 1 0 1 1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0
#>
#> $weights[[52]]
#> [1] 0 1 0 0 1 0 0 1 1 0 1 1 0 1 0 1 1 1 1 1 1
#>
#> $weights[[53]]
#> [1] 0 0 0 1 0 0 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1
#>
#> $weights[[54]]
#> [1] 1 0 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0
#>
#> $weights[[55]]
#> [1] 0 1 1 0 0 1 0 0 1 1 1 1 1 1 1 0 1 0 1 1 0
#>
#> $weights[[56]]
#> [1] 0 0 0 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 1
#>
#> $weights[[57]]
#> [1] 1 1 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 1 1 1
#>
#> $weights[[58]]
#> [1] 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 0 0 0 0 1 0
#>
#> $weights[[59]]
#> [1] 0 1 1 1 1 0 1 0 1 0 0 0 1 1 1 1 1 0 0 1 1
#>
#> $weights[[60]]
#> [1] 1 1 1 0 1 0 1 1 1 1 0 0 1 0 0 1 1 0 1 1 0
#>
#> $weights[[61]]
#> [1] 1 0 0 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1
#>
#> $weights[[62]]
#> [1] 1 1 0 1 1 0 0 1 1 1 0 1 1 1 1 0 0 1 1 0 0
#>
#> $weights[[63]]
#> [1] 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 0
#>
#> $weights[[64]]
#> [1] 0 0 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 0 1 0 1
#>
#> $weights[[65]]
#> [1] 0 1 0 1 1 1 0 1 0 1 0 1 1 0 0 1 1 0 1 1 1
#>
#> $weights[[66]]
#> [1] 0 0 1 1 1 1 0 1 1 0 0 1 1 1 1 0 0 1 1 1 0
#>
#> $weights[[67]]
#> [1] 1 0 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1
#>
#> $weights[[68]]
#> [1] 1 1 1 1 1 0 0 0 1 1 1 0 1 1 0 0 0 1 0 1 1
#>
#> $weights[[69]]
#> [1] 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 1 0 0 1
#>
#> $weights[[70]]
#> [1] 0 1 1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 1 1 0 1
#>
#> $weights[[71]]
#> [1] 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 1 1 1 0 1 0
#>
#> $weights[[72]]
#> [1] 0 1 1 1 0 0 1 1 1 0 1 0 0 1 0 1 1 1 0 1 1
#>
#> $weights[[73]]
#> [1] 1 1 0 0 1 1 1 1 1 1 0 1 1 0 1 0 0 0 1 0 1
#>
#> $weights[[74]]
#> [1] 1 1 0 1 0 1 1 0 1 1 1 0 1 0 1 0 1 0 1 0 1
#>
#> $weights[[75]]
#> [1] 1 0 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 1 0 1 1
#>
#> $weights[[76]]
#> [1] 0 1 1 0 1 0 1 0 0 1 1 1 1 0 1 1 1 0 1 1 0
#>
#> $weights[[77]]
#> [1] 1 1 1 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 1 0
#>
#> $weights[[78]]
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#>
#> $weights[[476]]
#> [1] 1 1 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 1 1 0 0
#>
#> $weights[[477]]
#> [1] 1 1 1 0 0 1 0 0 0 0 1 0 0 1 1 1 1 1 1 1 1
#>
#> $weights[[478]]
#> [1] 1 1 0 1 0 0 1 0 1 0 1 1 1 0 0 1 1 1 0 1 1
#>
#> $weights[[479]]
#> [1] 1 1 0 1 1 0 1 1 1 1 0 0 1 0 0 1 1 1 0 1 0
#>
#> $weights[[480]]
#> [1] 1 0 0 1 1 1 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0
#>
#> $weights[[481]]
#> [1] 0 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 1 1 1
#>
#> $weights[[482]]
#> [1] 1 0 0 1 1 0 1 1 1 1 0 0 0 1 0 1 1 0 1 1 1
#>
#> $weights[[483]]
#> [1] 1 0 0 1 1 1 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1
#>
#> $weights[[484]]
#> [1] 0 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 0 1 1 0 0
#>
#> $weights[[485]]
#> [1] 1 0 0 1 0 0 1 1 1 0 1 1 1 0 0 1 1 1 0 1 1
#>
#> $weights[[486]]
#> [1] 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 0 1 0 0
#>
#> $weights[[487]]
#> [1] 1 1 1 0 0 1 1 1 1 0 0 1 1 0 0 1 0 0 1 1 1
#>
#> $weights[[488]]
#> [1] 1 0 1 0 1 1 1 1 1 0 1 0 0 1 1 0 1 0 1 1 0
#>
#> $weights[[489]]
#> [1] 1 0 1 1 0 1 1 1 1 0 0 1 1 1 1 0 1 0 0 1 0
#>
#> $weights[[490]]
#> [1] 0 1 0 0 1 0 0 1 1 1 0 1 1 1 1 0 1 1 0 1 1
#>
#> $weights[[491]]
#> [1] 0 1 1 1 1 1 0 1 0 1 0 1 1 1 1 0 0 1 1 0 0
#>
#> $weights[[492]]
#> [1] 1 1 0 1 0 1 1 1 0 0 1 1 0 1 1 0 1 1 0 1 0
#>
#> $weights[[493]]
#> [1] 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 1 1
#>
#> $weights[[494]]
#> [1] 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 0 1 1 0 0
#>
#> $weights[[495]]
#> [1] 1 1 0 0 1 1 1 0 1 1 1 0 0 0 0 1 0 1 1 1 1
#>
#> $weights[[496]]
#> [1] 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1
#>
#> $weights[[497]]
#> [1] 1 0 1 0 1 0 1 1 1 0 1 0 1 0 1 0 1 1 1 1 0
#>
#> $weights[[498]]
#> [1] 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 1 0
#>
#> $weights[[499]]
#> [1] 1 0 1 1 1 0 0 1 1 0 0 1 1 1 1 0 0 0 1 1 1
#>
#> $weights[[500]]
#> [1] 1 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0
#>
#>
#> $fitted
#> idx (response)
#> 1 1 21.0
#> 2 2 21.0
#> 3 3 22.8
#> 4 4 21.4
#> 5 5 18.1
#> 6 6 22.8
#> 7 7 19.2
#> 8 8 16.4
#> 9 9 17.3
#> 10 10 15.2
#> 11 11 10.4
#> 12 12 10.4
#> 13 13 32.4
#> 14 14 33.9
#> 15 15 15.5
#> 16 16 15.2
#> 17 17 13.3
#> 18 18 27.3
#> 19 19 26.0
#> 20 20 19.7
#> 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: 0x55a44c13bb20>
#> <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: 0x55a458fb5070>
#> <environment: 0x55a468190528>
#>
#> $info$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: 0x55a458fb30e0>
#> <environment: 0x55a468190608>
#>
#> $info$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: 0x55a458fb5070>
#> <environment: 0x55a4681906e8>
#>
#> $info$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: 0x55a458fb30e0>
#> <environment: 0x55a468190838>
#>
#> $info$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: 0x55a458f7bc78>
#> <environment: 0x55a470bc4b10>
#>
#> $predictf
#> ~am + carb + cyl + disp + drat + gear + hp + qsec + vs + wt
#> attr(,"variables")
#> list(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)
#> attr(,"factors")
#> am carb cyl disp drat gear hp qsec vs wt
#> am 1 0 0 0 0 0 0 0 0 0
#> carb 0 1 0 0 0 0 0 0 0 0
#> cyl 0 0 1 0 0 0 0 0 0 0
#> disp 0 0 0 1 0 0 0 0 0 0
#> drat 0 0 0 0 1 0 0 0 0 0
#> gear 0 0 0 0 0 1 0 0 0 0
#> hp 0 0 0 0 0 0 1 0 0 0
#> qsec 0 0 0 0 0 0 0 1 0 0
#> vs 0 0 0 0 0 0 0 0 1 0
#> wt 0 0 0 0 0 0 0 0 0 1
#> attr(,"term.labels")
#> [1] "am" "carb" "cyl" "disp" "drat" "gear" "hp" "qsec" "vs" "wt"
#> attr(,"order")
#> [1] 1 1 1 1 1 1 1 1 1 1
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 0
#> attr(,"Formula_with_dot")
#> mpg ~ .
#> NULL
#> attr(,"Formula_without_dot")
#> mpg ~ am + carb + cyl + disp + drat + gear + hp + qsec + vs +
#> wt
#> NULL
#> attr(,"dot")
#> [1] "sequential"
#>
#> attr(,"class")
#> [1] "cforest" "constparties" "parties"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 31.66209