3456
939
weka.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_J48
1
Weka_3.7.13_11461
Weka implementation of AttributeSelectedClassifier
2016-04-15T00:57:02
English
Weka_3.7.13
-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
baselearner
weka.attributeSelection.InfoGainAttributeEval
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.Ranker
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.trees.J48
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.
num-decimal-places
option
The number of decimal places for the output of numbers in the model (default 2).
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
W
1720
1
weka.J48
33
Weka_3.7.13_11194
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
2015-10-30T12:49:47
English
Weka_3.7.13
-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
0.25
Set confidence threshold for pruning.
(default 0.25)
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
2
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.
Q
option
Seed for random data shuffling (default 1).
R
flag
Use reduced error pruning.
S
flag
Do not perform subtree raising.
U
flag
Use unpruned tree.
num-decimal-places
option
The number of decimal places for the output of numbers in the model (default 2).
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
mythbusting
mythbusting_1
study_15
study_20
study_34
study_73
Verified_Learning_Curve,Verified_Supervised_Classification
weka
weka_3.7.13
https://api.openml.org/data/download/1681027/weka.classifiers.trees.J481019998014146204943.class
class
5bd92b1eff2acdcf46b59e1af99f0850
S
2118
780
weka.Ranker
3
Weka_3.7.13_11213
Weka implementation of Ranker
2015-12-04T23:53:19
English
Weka_3.7.13
N
option
-1
Specify number of attributes to select
P
option
Specify a starting set of attributes.
Eg. 1,3,5-7.
Any starting attributes specified are
ignored during the ranking.
T
option
-1.7976931348623157E308
Specify a theshold by which attributes
may be discarded from the ranking.
weka
weka_3.7.13
E
2133
780
weka.InfoGainAttributeEval
2
Weka_3.7.13_10172
Weka implementation of InfoGainAttributeEval
2015-12-05T02:02:44
English
Weka_3.7.13
B
flag
just binarize numeric attributes instead
of properly discretizing them.
M
flag
treat missing values as a seperate value.
weka
weka_3.7.13
Verified_Supervised_Classification
weka
weka_3.7.13
https://api.openml.org/data/download/1912315/weka.classifiers.meta.AttributeSelectedClassifier2808826506435060598.class
class
e33297e05015e5c2e3533a8e686b98eb