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Uploaded 16-03-2017 by
Michael Torres
sklearn==0.18.1
numpy>=1.6.1
scipy>=0.9
52 runs
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Automatically created scikit-learn flow.
Parameters
boosting_type default: "gbdt" colsample_bytree default: 1 drop_rate default: 0.1 is_unbalance default: false learning_rate default: 0.1 max_bin default: 255 max_depth default: -1 max_drop default: 50 min_child_samples default: 10 min_child_weight default: 5 min_split_gain default: 0 n_estimators default: 10 nthread default: -1 num_leaves default: 31 objective default: "binary" reg_alpha default: 0 reg_lambda default: 0 scale_pos_weight default: 1 seed default: 0 sigmoid default: 1.0 silent default: true skip_drop default: 0.5 subsample default: 1 subsample_for_bin default: 50000 subsample_freq default: 1 uniform_drop default: false xgboost_dart_mode default: false
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Runs
Parameter:
none
boosting type
colsample bytree
drop rate
is unbalance
learning rate
max bin
max depth
max drop
min child samples
min child weight
min split gain
n estimators
nthread
num leaves
objective
reg alpha
reg lambda
scale pos weight
seed
sigmoid
silent
skip drop
subsample
subsample for bin
subsample freq
uniform drop
xgboost dart mode
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