835
266
weka.AttributeSelectedClassifier_WrapperSubsetEval_ZeroR_GreedyStepwise_Bagging_NaiveBayes
1
Weka_3.7.12-SNAPSHOT_10141
Weka implementation of AttributeSelectedClassifier
2014-12-02T14:54:10
English
Weka_3.7.12-SNAPSHOT
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
-doNotMakeSplitPointActualValue
flag
Do not make split point actual value.
A
flag
Laplace smoothing for predicted probabilities.
B
flag
Use binary splits only.
C
option
Set confidence threshold for pruning.
(default 0.25)
D
flag
Output debugging info.
E
kernel
weka.attributeSelection.WrapperSubsetEval
Full class name of attribute evaluator, followed
by its options.
eg: "weka.attributeSelection.CfsSubsetEval -L"
(default weka.attributeSelection.CfsSubsetEval)
J
flag
Do not use MDL correction for info gain on numeric attributes.
L
flag
Do not clean up after the tree has been built.
M
option
Set minimum number of instances per leaf.
(default 2)
N
option
Set number of folds for reduced error
pruning. One fold is used as pruning set.
(default 3)
O
flag
Do not collapse tree.
P
option
The size of the thread pool, for example, the number of cores in the CPU. (default 1)
Q
option
Seed for random data shuffling (default 1).
R
flag
Use reduced error pruning.
S
kernel
weka.attributeSelection.GreedyStepwise
Full class name of search method, followed
by its options.
eg: "weka.attributeSelection.BestFirst -D 1"
(default weka.attributeSelection.BestFirst)
U
flag
Use unpruned tree.
W
baselearner
weka.classifiers.meta.Bagging
Full name of base classifier.
(default: weka.classifiers.trees.J48)
Z
flag
Precompute the full correlation matrix at the outset, rather than compute correlations lazily (as needed) during the search. Use this in conjuction with parallel processing in order to speed up a backward search.
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
W
530
2
weka.Bagging_NaiveBayes
2
Weka_3.7.12-SNAPSHOT_10470
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2014-08-18T17:39:52
English
Weka_3.7.12-SNAPSHOT
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
-represent-copies-using-weights
flag
Represent copies of instances using weights rather than explicitly.
I
option
10
Number of iterations.
(default 10)
L
option
Maximum tree depth (default -1, no maximum)
M
option
Set minimum number of instances per leaf (default 2).
N
option
Number of folds for reduced error pruning (default 3).
O
flag
Calculate the out of bag error.
P
option
100
Size of each bag, as a percentage of the
training set size. (default 100)
R
flag
Spread initial count over all class values (i.e. don't use 1 per value)
S
option
1
Random number seed.
(default 1)
V
option
Set minimum numeric class variance proportion
of train variance for split (default 1e-3).
W
baselearner
weka.classifiers.bayes.NaiveBayes
Full name of base classifier.
(default: weka.classifiers.trees.REPTree)
num-slots
option
1
Number of execution slots.
(default 1 - i.e. no parallelism)
(use 0 to auto-detect number of cores)
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
W
380
1
weka.NaiveBayes
2
Weka_3.7.12-SNAPSHOT_10203
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
2014-08-16T20:13:11
English
Weka_3.7.12-SNAPSHOT
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
D
flag
Use supervised discretization to process numeric attributes
K
flag
Use kernel density estimator rather than normal
distribution for numeric attributes
O
flag
Display model in old format (good when there are many classes)
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
study_2
study_37
study_73
Verified_Learning_Curve,Verified_Supervised_Classification
https://api.openml.org/data/download/49847/weka.classifiers.bayes.NaiveBayes5738087145074490555.class
class
20dad3624d9becf0941822c151fd94cf
study_2
study_37
Verified_Learning_Curve,Verified_Supervised_Classification
https://api.openml.org/data/download/51564/weka.classifiers.meta.Bagging6907939767161094354.class
class
3d52ba6b5b5403d48c0882b91eb51076
E
793
266
weka.WrapperSubsetEval_ZeroR
1
Weka_3.7.12-SNAPSHOT_11215
Ron Kohavi, George H. John (1997). Wrappers for feature subset selection. Artificial Intelligence. 97(1-2):273-324.
2014-12-01T12:57:41
English
Weka_3.7.12-SNAPSHOT
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
B
baselearner
weka.classifiers.rules.ZeroR
class name of base learner to use for accuracy estimation.
Place any classifier options LAST on the command line
following a "--". eg.:
-B weka.classifiers.bayes.NaiveBayes ... -- -K
(default: weka.classifiers.rules.ZeroR)
E
option
acc
Performance evaluation measure to use for selecting attributes.
(Default = accuracy for discrete class and rmse for numeric class)
F
option
5
number of cross validation folds to use for estimating accuracy.
(default=5)
IRclass
option
Optional class value (label or 1-based index) to use in conjunction with
IR statistics (f-meas, auc or auprc). Omitting this option will use
the class-weighted average.
R
option
1
Seed for cross validation accuracy testimation.
(default = 1)
T
option
0.01
threshold by which to execute another cross validation
(standard deviation---expressed as a percentage of the mean).
(default: 0.01 (1%))
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
B
364
1
weka.ZeroR
2
Weka_3.7.12-SNAPSHOT_10153
Weka implementation of ZeroR
2014-08-10T22:12:05
English
Weka_3.7.12-SNAPSHOT
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
study_2
study_37
study_73
Verified_Learning_Curve,Verified_Supervised_Classification,Verified_Supervised_Regression
https://api.openml.org/data/download/49668/weka.classifiers.rules.ZeroR5729400676178878788.class
class
4498a30b6cfb13a39871f82552a445a4
weka
weka_3.7.12-SNAPSHOT
S
836
266
weka.GreedyStepwise
1
Weka_3.7.12-SNAPSHOT_11227
Weka implementation of GreedyStepwise
2014-12-02T14:54:10
English
Weka_3.7.12-SNAPSHOT
-B
flag
Use a backward search instead of a
forward one.
-C
flag
Use conservative forward search
-num-slots
option
The number of execution slots, for example, the number of cores in the CPU. (default 1)
D
flag
Print debugging output
N
option
-1
Specify number of attributes to select
P
option
Specify a starting set of attributes.
Eg. 1,3,5-7.
R
flag
Produce a ranked list of attributes.
T
option
-1.7976931348623157E308
Specify a theshold by which attributes
may be discarded from the ranking.
Use in conjuction with -R
weka
weka_3.7.12-SNAPSHOT
Verified_Supervised_Classification
weka
weka_3.7.12-SNAPSHOT
https://api.openml.org/data/download/249862/weka.classifiers.meta.AttributeSelectedClassifier8743138117756474463.class
class
254d1001fe710ef415a9c1bcaeaa7c51