6143
2742
weka.FilteredClassifier_Bagging_IBk
weka.classifiers.meta.FilteredClassifier
1
Weka_3.8.1_12647
Weka implementation of FilteredClassifier
2017-04-11T11:50:19
English
Weka_3.8.1
-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
option
weka.filters.supervised.attribute.Discretize -R first-last -precision 6
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.meta.Bagging
Full name of base classifier.
(default: weka.classifiers.trees.J48)
Y
flag
Use bin numbers rather than ranges for discretized attributes.
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).
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
6144
2742
weka.Bagging_IBk
weka.classifiers.meta.Bagging
6
Weka_3.8.1_13191
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2017-04-11T11:50:19
English
Weka_3.8.1
-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.
(current value 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).
O
flag
Calculate the out of bag error.
P
option
100
Size of each bag, as a percentage of the
training set size. (default 100)
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.lazy.IBk
Full name of base classifier.
(default: weka.classifiers.trees.REPTree)
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)
(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
output-out-of-bag-complexity-statistics
flag
Whether to output complexity-based statistics when out-of-bag evaluation is performed.
print
flag
Print the individual classifiers in the output
represent-copies-using-weights
flag
Represent copies of instances using weights rather than explicitly.
store-out-of-bag-predictions
flag
Whether to store out of bag predictions in internal evaluation object.
W
5918
2735
weka.IBk
weka.classifiers.lazy.IBk
11
Weka_3.8.1_10141
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
2017-03-28T23:37:51
English
Weka_3.8.1
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
A
flag
true
The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
E
flag
Minimise mean squared error rather than mean absolute
error when using -X option with numeric prediction.
F
flag
Weight neighbours by 1 - their distance
(use when k > 1)
I
flag
Weight neighbours by the inverse of their distance
(use when k > 1)
K
option
1
Number of nearest neighbours (k) used in classification.
(Default = 1)
W
option
0
Maximum number of training instances maintained.
Training instances are dropped FIFO. (Default = no window)
X
flag
Select the number of nearest neighbours between 1
and the k value specified using hold-one-out evaluation
on the training data (use when k > 1)
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).
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
Verified_Supervised_Classification
weka
weka_3.8.1
https://api.openml.org/data/download/4691512/weka.classifiers.lazy.IBk6633667906490039877.class
class
dadc825a6c0e0b12bc812872258448e6
weka
weka_3.8.1
Verified_Supervised_Classification
weka
weka_3.8.1