6906
1856
weka.FilteredClassifier_AttributeSelectedClassifier_Bagging_OneR
weka.classifiers.meta.FilteredClassifier
1
Weka_3.8.1_12647
Weka implementation of FilteredClassifier
2017-07-05T00:54:51
English
Weka_3.8.1
-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 binary attributes for discretized attributes.
E
flag
Use better encoding of split point for MDL.
F
option
weka.filters.supervised.attribute.Discretize -R first-last -precision 6
Full class name of filter to use, followed
by filter options.
eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
J
flag
Do not use MDL correction for info gain on numeric attributes.
K
flag
Use Kononenko's MDL criterion.
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
flag
Do not perform subtree raising.
U
flag
Use unpruned tree.
V
flag
Invert matching sense of column indexes.
W
baselearner
weka.classifiers.meta.AttributeSelectedClassifier
Full name of base classifier.
(default: weka.classifiers.trees.J48)
Y
flag
Use bin numbers rather than ranges for discretized attributes.
batch-size
option
The desired batch size for batch prediction (default 100).
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
precision
option
Precision for bin boundary labels.
(default = 6 decimal places).
W
6907
1856
weka.AttributeSelectedClassifier_Bagging_OneR
weka.classifiers.meta.AttributeSelectedClassifier
2
Weka_3.8.1_11461
Weka implementation of AttributeSelectedClassifier
2017-07-05T00:54:51
English
Weka_3.8.1
-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
option
weka.attributeSelection.CfsSubsetEval -P 1 -E 1
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
option
weka.attributeSelection.BestFirst -D 1 -N 5
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.
batch-size
option
The desired batch size for batch prediction (default 100).
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
6822
1856
weka.Bagging_OneR
weka.classifiers.meta.Bagging
14
Weka_3.8.1_13191
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2017-07-02T21:04:24
English
Weka_3.8.1
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
I
option
10
Number of iterations.
(current value 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.rules.OneR
Full name of base classifier.
(default: weka.classifiers.trees.REPTree)
batch-size
option
The desired batch size for batch prediction (default 100).
num-decimal-places
option
The number of decimal places for the output of numbers in the model (default 2).
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
output-out-of-bag-complexity-statistics
flag
Whether to output complexity-based statistics when out-of-bag evaluation is performed.
print
flag
Print the individual classifiers in the output
represent-copies-using-weights
flag
Represent copies of instances using weights rather than explicitly.
store-out-of-bag-predictions
flag
Whether to store out of bag predictions in internal evaluation object.
W
5942
2773
weka.OneR
weka.classifiers.rules.OneR
20
Weka_3.8.1_10153
R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
2017-03-30T15:15:17
English
Weka_3.8.1
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
B
option
6
The minimum number of objects in a bucket (default: 6).
batch-size
option
The desired batch size for batch prediction (default 100).
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
Verified_Supervised_Classification
weka
weka_3.8.1
https://api.openml.org/data/download/4699319/weka.classifiers.rules.OneR1511221561521817134.class
class
3d3f29f94353759bf757b3060bee87e3
weka
weka_3.8.1
weka
weka_3.8.1
Verified_Supervised_Classification
weka
weka_3.8.1
https://api.openml.org/data/download/9246076/weka.classifiers.meta.FilteredClassifier2794947458946197592.class
class
273f4ff71122cbc074274fd619c61411