190
2
weka.MultiBoostAB_BFTree
1
Weka_3.7.10_8109
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
2014-06-03T15:12:32
English
Weka_3.7.10
C
option
3
Number of sub-committees. (Default 3)
D
flag
If set, classifier is run in debug mode and
may output additional info to the console
I
option
10
Number of iterations.
(default 10)
P
option
100
Percentage of weight mass to base training on.
(default 100, reduce to around 90 speed up)
Q
flag
Use resampling for boosting.
S
option
1
Random number seed.
(default 1)
W
baselearner
weka.classifiers.trees.BFTree
Full name of base classifier.
(default: weka.classifiers.trees.DecisionStump)
W
160
2
weka.BFTree
1
Weka_3.7.10_8109
Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ.
Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.
2014-06-03T14:58:27
English
Weka_3.7.10
A
flag
Use the 1 SE rule to make pruning decision.
(default no).
C
flag
true
Percentage of training data size (0-1]
(default 1).
D
flag
If set, classifier is run in debug mode and
may output additional info to the console
G
flag
Don't use Gini index for splitting (default yes),
if not information is used.
H
flag
Don't use heuristic search for nominal attributes in multi-class
problem (default yes).
M
option
2
The minimal number of instances at the terminal nodes.
(default 2)
N
option
5
The number of folds used in the pruning.
(default 5)
P
option
POSTPRUNED
The pruning strategy.
(default: POSTPRUNED)
R
flag
Don't use error rate in internal cross-validation (default yes),
but root mean squared error.
S
option
1
Random number seed.
(default 1)
Verified_Supervised_Classification
Verified_Supervised_Classification
https://api.openml.org/data/download/30276/weka.classifiers.meta.MultiBoostAB6310602320943099694.class
class
ad2a381e81aa8a5e7013df93dc41794c