6145
2742
weka.FilteredClassifier_FilteredClassifier_FilteredClassifier_Bagging_SMO_PolyKernel
weka.classifiers.meta.FilteredClassifier
1
Weka_3.8.1_12647
Weka implementation of FilteredClassifier
2017-04-11T11:50:55
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.FilteredClassifier
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
6146
2742
weka.FilteredClassifier_FilteredClassifier_Bagging_SMO_PolyKernel
weka.classifiers.meta.FilteredClassifier
1
Weka_3.8.1_12647
Weka implementation of FilteredClassifier
2017-04-11T11:50:55
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.FilteredClassifier
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
6147
2742
weka.FilteredClassifier_Bagging_SMO_PolyKernel
weka.classifiers.meta.FilteredClassifier
1
Weka_3.8.1_12647
Weka implementation of FilteredClassifier
2017-04-11T11:50:55
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.Bagging
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
6148
2742
weka.Bagging_SMO_PolyKernel
weka.classifiers.meta.Bagging
3
Weka_3.8.1_13191
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2017-04-11T11:50:55
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.functions.SMO
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
5926
2735
weka.SMO_PolyKernel
weka.classifiers.functions.SMO
15
Weka_3.8.1_12558
J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, 1998.
S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy (2001). Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation. 13(3):637-649.
Trevor Hastie, Robert Tibshirani: Classification by Pairwise Coupling. In: Advances in Neural Information Processing Systems, 1998.
2017-03-29T00:39:19
English
Weka_3.8.1
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
C
option
1.0
The complexity constant C. (default 1)
E
option
The Exponent to use.
(default: 1.0)
K
kernel
weka.classifiers.functions.supportVector.PolyKernel
The Kernel to use.
(default: weka.classifiers.functions.supportVector.PolyKernel)
L
option
0.001
The tolerance parameter. (default 1.0e-3)
M
flag
Fit calibration models to SVM outputs.
N
option
0
Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
P
option
1.0E-12
The epsilon for round-off error. (default 1.0e-12)
R
option
Set the ridge in the log-likelihood.
V
option
-1
The number of folds for the internal
cross-validation. (default -1, use training data)
W
option
1
The random number seed. (default 1)
batch-size
option
The desired batch size for batch prediction (default 100).
calibrator
flag
true
Full name of calibration model, followed by options.
(default: "weka.classifiers.functions.Logistic")
no-checks
flag
Turns off all checks - use with caution!
Turning them off assumes that data is purely numeric, doesn't
contain any missing values, and has a nominal class. Turning them
off also means that no header information will be stored if the
machine is linear. Finally, it also assumes that no instance has
a weight equal to 0.
(default: checks on)
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
K
5927
2735
weka.PolyKernel
weka.classifiers.functions.supportVector.PolyKernel
11
Weka_3.8.1_12533
Weka implementation of PolyKernel
2017-03-29T00:39:19
English
Weka_3.8.1
C
option
250007
The size of the cache (a prime number), 0 for full cache and
-1 to turn it off.
(default: 250007)
E
option
1.0
The Exponent to use.
(default: 1.0)
L
flag
Use lower-order terms.
(default: no)
no-checks
flag
Turns off all checks - use with caution!
(default: checks on)
output-debug-info
flag
Enables debugging output (if available) to be printed.
(default: off)
weka
weka_3.8.1
study_73
Verified_Supervised_Classification
weka
weka_3.8.1
https://api.openml.org/data/download/4691858/weka.classifiers.functions.SMO3463712247787693537.class
class
9bd3e7b0336ebcf8546734ec2cedfe2e
weka
weka_3.8.1
weka
weka_3.8.1
weka
weka_3.8.1
Verified_Supervised_Classification
weka
weka_3.8.1
https://api.openml.org/data/download/5030732/weka.classifiers.meta.FilteredClassifier6503378644787203220.class
class
d36a54655368c6b3970f0bd7a40dda83