6313
2742
weka.CostSensitiveClassifier_FilteredClassifier_SMO_PolyKernel
weka.classifiers.meta.CostSensitiveClassifier
1
Weka_3.8.1_12180
Weka implementation of CostSensitiveClassifier
2017-04-17T17:03:13
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
File name of a cost matrix to use. If this is not supplied,
a cost matrix will be loaded on demand. The name of the
on-demand file is the relation name of the training data
plus ".cost", and the path to the on-demand file is
specified with the -N option.
M
flag
Minimize expected misclassification cost. Default is to
reweight training instances according to costs per class
N
option
C:\Program Files\Weka-3-8
Name of a directory to search for cost files when loading
costs on demand (default current directory).
S
option
1
Random number seed.
(default 1)
W
baselearner
weka.classifiers.meta.FilteredClassifier
Full name of base classifier.
(default: weka.classifiers.rules.ZeroR)
batch-size
option
The desired batch size for batch prediction (default 100).
cost-matrix
option
The cost matrix in Matlab single line format.
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
6183
2775
weka.FilteredClassifier_SMO_PolyKernel
weka.classifiers.meta.FilteredClassifier
1
Weka_3.8.1_12647
Weka implementation of FilteredClassifier
2017-04-12T14:11:16
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.functions.SMO
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
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
Verified_Supervised_Classification
weka
weka_3.8.1
https://api.openml.org/data/download/5031044/weka.classifiers.meta.FilteredClassifier5494920847734709921.class
class
d36a54655368c6b3970f0bd7a40dda83
Verified_Supervised_Classification
weka
weka_3.8.1
https://api.openml.org/data/download/5035621/weka.classifiers.meta.CostSensitiveClassifier2190300404400233041.class
class
1bd6d627a37903549b97baa041c4d2c2