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Uploaded 04-04-2019 by
Isaac Rodriguez
R_3.5.3, OpenML_1.9, mlr_2.13.9000, xgboost_0.82.1
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Learner mlr.classif.xgboost from package(s) xgboost.
Parameters
alpha base_score booster callbacks colsample_bylevel colsample_bytree early_stopping_rounds eta eval_metric feval gamma lambda lambda_bias max_delta_step max_depth maximize min_child_weight missing monotone_constraints normalize_type nrounds nthread num_parallel_tree objective openml.kind default: Mersenne-Twister openml.normal.kind default: Inversion openml.seed default: 1 print_every_n rate_drop sample_type skip_drop subsample tweedie_variance_power verbose watchlist
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Runs
Parameter:
none
alpha
base score
booster
callbacks
colsample bylevel
colsample bytree
early stopping rounds
eta
eval metric
feval
gamma
lambda
lambda bias
max delta step
max depth
maximize
min child weight
missing
monotone constraints
normalize type
nrounds
nthread
num parallel tree
objective
openml.kind
openml.normal.kind
openml.seed
print every n
rate drop
sample type
skip drop
subsample
tweedie variance power
verbose
watchlist
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