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Uploaded 29-03-2017 by
Heidi Seibold
R_3.3.3, OpenML_1.2, mlr_2.10, randomForestSRC_2.4.2
10 runs
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Learner mlr.classif.randomForestSRC from package(s) randomForestSRC.
Parameters
bootstrap do.trace forest importance membership mtry na.action nimpute nodedepth nodesize nsplit ntree openml.kind default: Mersenne-Twister openml.normal.kind default: Inversion openml.seed default: 1 proximity sampsize samptype seed split.depth split.null splitrule statistics tree.err var.used xvar.wt
0
Runs
Parameter:
none
bootstrap
do.trace
forest
importance
membership
mtry
na.action
nimpute
nodedepth
nodesize
nsplit
ntree
openml.kind
openml.normal.kind
openml.seed
proximity
sampsize
samptype
seed
split.depth
split.null
splitrule
statistics
tree.err
var.used
xvar.wt
Supervised Classification
Supervised Regression
Learning Curve
Supervised Data Stream Classification
Clustering
Machine Learning Challenge
Survival Analysis
Subgroup Discovery
area under roc curve
average cost
binominal test
build cpu time
build memory
c index
chi-squared
class complexity
class complexity gain
confusion matrix
correlation coefficient
cortana quality
coverage
f measure
information gain
jaccard
kappa
kb relative information score
kohavi wolpert bias squared
kohavi wolpert error
kohavi wolpert sigma squared
kohavi wolpert variance
kononenko bratko information score
matthews correlation coefficient
mean absolute error
mean class complexity
mean class complexity gain
mean f measure
mean kononenko bratko information score
mean precision
mean prior absolute error
mean prior class complexity
mean recall
mean weighted area under roc curve
mean weighted f measure
mean weighted precision
weighted recall
number of instances
os information
positives
precision
predictive accuracy
prior class complexity
prior entropy
probability
quality
ram hours
recall
relative absolute error
root mean prior squared error
root mean squared error
root relative squared error
run cpu time
run memory
run virtual memory
scimark benchmark
single point area under roc curve
total cost
unclassified instance count
usercpu time millis
usercpu time millis testing
usercpu time millis training
webb bias
webb error
webb variance
joint entropy
pattern team auroc10
wall clock time millis
wall clock time millis training
wall clock time millis testing
unweighted recall