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Uploaded 02-04-2017 by
Weka_3.8.1
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Weka implementation of BayesNet
Parameters
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built
(use with caution). A Initial count (alpha) B BIF file to compare with D Do not use ADTree data structure default: true E Estimator algorithm N Initial structure is empty (instead of Naive Bayes) P Maximum number of parents Q Search algorithm default: weka.classifiers.bayes.net.search.local.K2 R Random order.
(default false) S Score type (BAYES, BDeu, MDL, ENTROPY and AIC) batch-size The desired batch size for batch prediction (default 100). mbc Applies a Markov Blanket correction to the network structure,
after a network structure is learned. This ensures that all
nodes in the network are part of the Markov blanket of the
classifier node. num-decimal-places The number of decimal places for the output of numbers in the model (default 2). output-debug-info If set, classifier is run in debug mode and
may output additional info to the console
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Parameter:
none
-do-not-check-capabilities
A
B
D
E
N
P
Q
R
S
batch-size
mbc
num-decimal-places
output-debug-info
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