Visibility: public
Uploaded 18-03-2017 by
Joost Visser
sklearn==0.18.1
numpy>=1.6.1
scipy>=0.9
13 runs
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Automatically created scikit-learn flow.
Parameters
gbc__criterion default: "friedman_mse" gbc__init default: null gbc__learning_rate default: 0.01 gbc__loss default: "deviance" gbc__max_depth default: 8 gbc__max_features default: "sqrt" gbc__max_leaf_nodes default: null gbc__min_impurity_split default: 1e-07 gbc__min_samples_leaf default: 2 gbc__min_samples_split default: 2 gbc__min_weight_fraction_leaf default: 0.0 gbc__n_estimators default: 24 gbc__presort default: "auto" gbc__random_state default: null gbc__subsample default: 1.0 gbc__verbose default: 0 gbc__warm_start default: false lclf__boosting_type default: "dart" lclf__colsample_bytree default: 1 lclf__drop_rate default: 0.08 lclf__is_unbalance default: false lclf__learning_rate default: 0.02 lclf__max_bin default: 255 lclf__max_depth default: -1 lclf__max_drop default: 50 lclf__min_child_samples default: 10 lclf__min_child_weight default: 5 lclf__min_split_gain default: 0 lclf__n_estimators default: 24 lclf__nthread default: -1 lclf__num_leaves default: 128 lclf__objective default: "binary" lclf__reg_alpha default: 1e-06 lclf__reg_lambda default: 1 lclf__scale_pos_weight default: 1 lclf__seed default: 0 lclf__sigmoid default: 1.0 lclf__silent default: true lclf__skip_drop default: 0.4 lclf__subsample default: 1 lclf__subsample_for_bin default: 50000 lclf__subsample_freq default: 1 lclf__uniform_drop default: false lclf__xgboost_dart_mode default: true rfc__bootstrap default: true rfc__class_weight default: null rfc__criterion default: "entropy" rfc__max_depth default: null rfc__max_features default: 0.1 rfc__max_leaf_nodes default: null rfc__min_impurity_split default: 1e-07 rfc__min_samples_leaf default: 2 rfc__min_samples_split default: 2 rfc__min_weight_fraction_leaf default: 0.0 rfc__n_estimators default: 24 rfc__n_jobs default: -1 rfc__oob_score default: false rfc__random_state default: null rfc__verbose default: 0 rfc__warm_start default: false xgb__base_score default: 0.5 xgb__colsample_bylevel default: 1 xgb__colsample_bytree default: 0.9 xgb__gamma default: 0 xgb__learning_rate default: 0.01 xgb__max_delta_step default: 0 xgb__max_depth default: 8 xgb__min_child_weight default: 2 xgb__missing default: null xgb__n_estimators default: 24 xgb__nthread default: -1 xgb__objective default: "binary:logistic" xgb__reg_alpha default: 0 xgb__reg_lambda default: 1 xgb__scale_pos_weight default: 1 xgb__seed default: 0 xgb__silent default: true xgb__subsample default: 0.8
0
Runs
Parameter:
none
gbc criterion
gbc init
gbc learning rate
gbc loss
gbc max depth
gbc max features
gbc max leaf nodes
gbc min impurity split
gbc min samples leaf
gbc min samples split
gbc min weight fraction leaf
gbc n estimators
gbc presort
gbc random state
gbc subsample
gbc verbose
gbc warm start
lclf boosting type
lclf colsample bytree
lclf drop rate
lclf is unbalance
lclf learning rate
lclf max bin
lclf max depth
lclf max drop
lclf min child samples
lclf min child weight
lclf min split gain
lclf n estimators
lclf nthread
lclf num leaves
lclf objective
lclf reg alpha
lclf reg lambda
lclf scale pos weight
lclf seed
lclf sigmoid
lclf silent
lclf skip drop
lclf subsample
lclf subsample for bin
lclf subsample freq
lclf uniform drop
lclf xgboost dart mode
rfc bootstrap
rfc class weight
rfc criterion
rfc max depth
rfc max features
rfc max leaf nodes
rfc min impurity split
rfc min samples leaf
rfc min samples split
rfc min weight fraction leaf
rfc n estimators
rfc n jobs
rfc oob score
rfc random state
rfc verbose
rfc warm start
xgb base score
xgb colsample bylevel
xgb colsample bytree
xgb gamma
xgb learning rate
xgb max delta step
xgb max depth
xgb min child weight
xgb missing
xgb n estimators
xgb nthread
xgb objective
xgb reg alpha
xgb reg lambda
xgb scale pos weight
xgb seed
xgb silent
xgb subsample
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