6422
2749
weka.Stacking_SMO
weka.classifiers.meta.Stacking
2
Weka_3.9.1_12205
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
2017-04-21T11:41:18
English
Weka_3.9.1
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
B
baselearner
weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K "weka.classifiers.functions.supportVector.RBFKernel -G 0.01 -C 250007" -calibrator "weka.classifiers.functions.Logistic -R 1.0E-8 -M -1 -num-decimal-places 4"
Full class name of classifier to include, followed
by scheme options. May be specified multiple times.
(default: "weka.classifiers.rules.ZeroR")
M
flag
true
Full name of meta classifier, followed by options.
(default: "weka.classifiers.rules.Zero")
S
option
1
Random number seed.
(default 1)
X
option
10
Sets the number of cross-validation folds.
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)
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
B
6423
2749
weka.SMO
weka.classifiers.functions.SMO
3
Weka_3.9.1_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.
2017-04-21T11:41:18
English
Weka_3.9.1
-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
option
weka.classifiers.functions.supportVector.PolyKernel -E 1.0 -C 250007
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
weka
weka_3.9.1
Verified_Supervised_Classification
weka
weka_3.9.1
https://api.openml.org/data/download/5115726/weka.classifiers.meta.Stacking4197903226761739069.class
class
cf47d1dfd30feb73d9f81c1d00462cc9