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Uploaded 29-11-2018 by
Michael Torres
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
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Automatically created scikit-learn flow.
Parameters
boosting_type default: "gbdt" class_weight default: null colsample_bytree default: 1.0 importance_type default: "split" lambda_l1 default: 0 lambda_l2 default: 0 learning_rate default: 0.01 max_depth default: -1 min_child_samples default: 20 min_child_weight default: 0.0001 min_split_gain default: 0.0 n_estimators default: 100 n_jobs default: -1 num_leaves default: 4 objective default: null random_state default: null reg_alpha default: 0.0 reg_lambda default: 0.0 silent default: true subsample default: 1.0 subsample_for_bin default: 200000 subsample_freq default: 0 verbose default: -1
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Parameter:
none
boosting type
class weight
colsample bytree
importance type
lambda l1
lambda l2
learning rate
max depth
min child samples
min child weight
min split gain
n estimators
n jobs
num leaves
objective
random state
reg alpha
reg lambda
silent
subsample
subsample for bin
subsample freq
verbose
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