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Uploaded 14-03-2019 by
Gil Hoben
shogun==6.2.0
numpy>=1.7.0
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Automatically created shogun flow.
Parameters
bias Bias b. default: 0.0 complete Convergence status default: null continue_features Continue Features default: null current_iteration Current Iteration of training default: 0 data_locked Indicates whether data is locked default: null features Feature object. default: null initialize_hyperplane Whether to initialize hyperplane. default: null labels Labels to be used. default: null learn_rate Learning rate. default: 0.1 max_iterations Maximum number of Iterations default: 1000 max_train_time Maximum training time. default: 0.0 solver_type Type of solver. default: "ST_AUTO" store_model_features Should feature data of model be stored after training? default: null w Parameter vector w.
0
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Parameter:
none
bias
complete
continue features
current iteration
data locked
features
initialize hyperplane
labels
learn rate
max iterations
max train time
solver type
store model features
w
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