2051
824
weka.LogitBoost_AttributeSelectedClassifier_CfsSubsetEval_BestFirst_J48
1
Weka_3.7.13_11958
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
2015-12-03T12:04:48
English
Weka_3.7.13
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
E
option
1
The number of threads to use for batch prediction, which should be >= size of thread pool.
(default 1)
H
option
1.0
Shrinkage parameter.
(default 1)
I
option
10
Number of iterations.
(default 10)
L
option
-1.7976931348623157E308
Threshold on the improvement of the likelihood.
(default -Double.MAX_VALUE)
O
option
1
The size of the thread pool, for example, the number of cores in the CPU. (default 1)
P
option
100
Percentage of weight mass to base training on.
(default 100, reduce to around 90 speed up)
Q
flag
Use resampling instead of reweighting for boosting.
S
option
1
Random number seed.
(default 1)
W
baselearner
weka.classifiers.meta.AttributeSelectedClassifier
Full name of base classifier.
(default: weka.classifiers.trees.DecisionStump)
Z
option
3.0
Z max threshold for responses.
(default 3)
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
2052
824
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_J48
5
Weka_3.7.13_11461
Weka implementation of AttributeSelectedClassifier
2015-12-03T12:04:48
English
Weka_3.7.13
-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
baselearner
weka.attributeSelection.CfsSubsetEval
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
baselearner
weka.attributeSelection.BestFirst
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.trees.J48
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.
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
1720
1
weka.J48
33
Weka_3.7.13_11194
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
2015-10-30T12:49:47
English
Weka_3.7.13
-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.
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
mythbusting
mythbusting_1
study_15
study_20
study_34
study_73
Verified_Learning_Curve,Verified_Supervised_Classification
weka
weka_3.7.13
https://api.openml.org/data/download/1681027/weka.classifiers.trees.J481019998014146204943.class
class
5bd92b1eff2acdcf46b59e1af99f0850
E
2049
824
weka.CfsSubsetEval
5
Weka_3.7.13_11852
M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand.
2015-12-03T12:02:12
English
Weka_3.7.13
D
flag
Output debugging info.
E
option
1
The number of threads to use, which should be >= size of thread pool. (default 1)
L
flag
Don't include locally predictive attributes.
M
flag
Treat missing values as a separate value.
P
option
1
The size of the thread pool, for example, the number of cores in the CPU. (default 1)
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.
weka
weka_3.7.13
S
2050
824
weka.BestFirst
4
Weka_3.7.13_10396
Weka implementation of BestFirst
2015-12-03T12:02:12
English
Weka_3.7.13
D
option
1
Direction of search. (default = 1).
N
option
5
Number of non-improving nodes to
consider before terminating search.
P
option
Specify a starting set of attributes.
Eg. 1,3,5-7.
S
option
Size of lookup cache for evaluated subsets.
Expressed as a multiple of the number of
attributes in the data set. (default = 1)
weka
weka_3.7.13
weka
weka_3.7.13
weka
weka_3.7.13
https://api.openml.org/data/download/1726316/weka.classifiers.meta.LogitBoost3212746527820931670.class
class
125fe543b65a89a18a591a7239db5809