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Uploaded 16-04-2019 by
Jason
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
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Automatically created scikit-learn flow.
Parameters
criterion default: "mse" init default: null learning_rate default: 0.04237820218764428 loss default: "deviance" max_depth default: 2 max_features default: 0.7003012722785171 max_leaf_nodes default: null min_impurity_decrease default: 0.3972654798282119 min_impurity_split default: null min_samples_leaf default: 9 min_samples_split default: 6 min_weight_fraction_leaf default: 0.15618023293782252 n_estimators default: 91 n_iter_no_change default: 1775 presort default: "auto" random_state default: null subsample default: 0.17296164825655347 tol default: 0.01906770694882285 validation_fraction default: 0.8228668135316636 verbose default: 0 warm_start default: false
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Parameter:
none
criterion
init
learning rate
loss
max depth
max features
max leaf nodes
min impurity decrease
min impurity split
min samples leaf
min samples split
min weight fraction leaf
n estimators
n iter no change
presort
random state
subsample
tol
validation fraction
verbose
warm start
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