1163
225
weka.FilteredClassifier_PrincipalComponents_J48
1
Weka_3.7.12_11142
Weka implementation of FilteredClassifier
2015-02-16T18:44:11
English
Weka_3.7.12
-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
kernel
weka.filters.unsupervised.attribute.PrincipalComponents
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.trees.J48
Full name of base classifier.
(default: weka.classifiers.trees.J48)
Y
flag
Use bin numbers rather than ranges for discretized attributes.
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
1068
1
weka.J48
28
Weka_3.7.12_11194
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
2015-02-05T16:06:52
English
Weka_3.7.12
-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
0.25
Set confidence threshold for pruning.
(default 0.25)
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
2
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.
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
study_1
study_41
study_73
Verified_Learning_Curve,Verified_Supervised_Classification
weka
weka_3.7.12
https://api.openml.org/data/download/483041/weka.classifiers.trees.J481794474309767094055.class
class
5bd92b1eff2acdcf46b59e1af99f0850
F
1164
225
weka.PrincipalComponents
12
Weka_3.7.12_10215
Weka implementation of PrincipalComponents
2015-02-16T18:44:11
English
Weka_3.7.12
A
option
5
Maximum number of attributes to include in
transformed attribute names.
(-1 = include all, default: 5)
C
flag
Center (rather than standardize) the
data and compute PCA using the covariance (rather
than the correlation) matrix.
M
option
-1
Maximum number of PC attributes to retain.
(-1 = include all, default: -1)
R
option
0.95
Retain enough PC attributes to account
for this proportion of variance in the original data.
(default: 0.95)
weka
weka_3.7.12
study_1
study_41
Verified_Learning_Curve,Verified_Supervised_Classification
weka
weka_3.7.12
https://api.openml.org/data/download/533690/weka.classifiers.meta.FilteredClassifier5391861402654827492.class
class
926b06b5048916807784bd1bda4434ef