957
265
weka.LogitBoost_RandomSubSpace_LinearRegression
1
Weka_3.7.12-SNAPSHOT_10965
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
2014-12-07T18:43:25
English
Weka_3.7.12-SNAPSHOT
-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.RandomSubSpace
Full name of base classifier.
(default: weka.classifiers.trees.DecisionStump)
Z
option
3.0
Z max threshold for responses.
(default 3)
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
W
958
265
weka.RandomSubSpace_LinearRegression
1
Weka_3.7.12-SNAPSHOT_10470
Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL http://citeseer.ist.psu.edu/ho98random.html.
2014-12-07T18:43:25
English
Weka_3.7.12-SNAPSHOT
-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.
(default 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).
P
option
0.5
Size of each subspace:
< 1: percentage of the number of attributes
>=1: absolute number of attributes
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.LinearRegression
Full name of base classifier.
(default: weka.classifiers.trees.REPTree)
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
W
686
1
weka.LinearRegression
4
Weka_3.7.12-SNAPSHOT_11128
Weka implementation of LinearRegression
2014-11-03T18:50:37
English
Weka_3.7.12-SNAPSHOT
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
C
flag
Do not try to eliminate colinear attributes.
R
option
1.0E-8
Set ridge parameter (default 1.0e-8).
S
option
0
Set the attribute selection method to use. 1 = None, 2 = Greedy.
(default 0 = M5' method)
additional-stats
flag
Output additional statistics.
minimal
flag
Conserve memory, don't keep dataset header and means/stdevs.
Model cannot be printed out if this option is enabled. (default: keep data)
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
Verified_Learning_Curve
weka
weka_3.7.12-SNAPSHOT
https://api.openml.org/data/download/120998/weka.classifiers.functions.LinearRegression6350462760096144059.class
class
9a3e00472ba5600eeef509c84bb09050
weka
weka_3.7.12-SNAPSHOT
weka
weka_3.7.12-SNAPSHOT
https://api.openml.org/data/download/268044/weka.classifiers.meta.LogitBoost7145717625213438165.class
class
213560bbc87618ef618c61e25d09d2f4