295
2
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_A1DE
1
Weka_3.7.10_9186
Weka implementation of AttributeSelectedClassifier
2014-06-03T22:43:36
English
Weka_3.7.10
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
If set, classifier is run in debug mode and
may output additional info to the console
E
kernel
weka.attributeSelection.CfsSubsetEval
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.
Q
option
Seed for random data shuffling (default 1).
R
flag
Use reduced error pruning.
S
kernel
weka.attributeSelection.BestFirst
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.bayes.AveragedNDependenceEstimators.A1DE
Full name of base classifier.
(default: weka.classifiers.trees.J48)
E
134
2
weka.CfsSubsetEval
1
Weka_3.7.10_9883
M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand.
2014-05-15T00:21:40
English
Weka_3.7.10
D
flag
Output debugging info.
E
option
1
The number of threads to use, which should be >= size of thread pool. (default 1)
L
flag
Don't include locally predictive attributes.
M
flag
Treat missing values as a separate value.
P
option
1
The size of the thread pool, for example, the number of cores in the CPU. (default 1)
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.
S
135
2
weka.BestFirst
1
Weka_3.7.10_8034
Weka implementation of BestFirst
2014-05-15T00:21:40
English
Weka_3.7.10
D
option
1
Direction of search. (default = 1).
N
option
5
Number of non-improving nodes to
consider before terminating search.
P
option
Specify a starting set of attributes.
Eg. 1,3,5-7.
S
option
Size of lookup cache for evaluated subsets.
Expressed as a multiple of the number of
attributes in the data set. (default = 1)
W
225
2
weka.A1DE
1
Weka_3.7.10_5516
G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
2014-06-03T15:26:14
English
Weka_3.7.10
D
flag
Output debugging information
F
option
1
Impose a frequency limit for superParents (default is 1)
M
option
1.0
Specify a weight to use with m-estimate (default is 1)
S
option
Specify a critical value for specialization-generalilzation SR (default is 100)
W
flag
Specify if to use weighted AODE
Verified_Supervised_Classification
https://api.openml.org/data/download/30531/weka.classifiers.bayes.AveragedNDependenceEstimators.A1DE8657113863766566408.class
class
37425c3ff0e8e605aa11bbed5772d537
Verified_Supervised_Classification
https://api.openml.org/data/download/30888/weka.classifiers.meta.AttributeSelectedClassifier8345149132676471865.class
class
ea027543ba0b8bdda558e1d11361196e