5237
1995
weka.FilteredClassifier_RandomForest
3
Weka_3.8.0_12647
Weka implementation of FilteredClassifier
2016-12-04T22:53:40
English
Weka_3.8.0
-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.trees.RandomForest
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
5238
1995
weka.RandomForest
11
Weka_3.8.0_12645
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
2016-12-04T22:53:40
English
Weka_3.8.0
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
B
flag
Break ties randomly when several attributes look equally good.
I
option
100
Number of iterations.
(current value 100)
K
option
0
Number of attributes to randomly investigate. (default 0)
(<1 = int(log_2(#predictors)+1)).
M
option
1.0
Set minimum number of instances per leaf.
(default 1)
N
option
Number of folds for backfitting (default 0, no backfitting).
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)
S
option
1
Seed for random number generator.
(default 1)
U
flag
Allow unclassified instances.
V
option
0.001
Set minimum numeric class variance proportion
of train variance for split (default 1e-3).
batch-size
option
The desired batch size for batch prediction (default 100).
depth
option
The maximum depth of the tree, 0 for unlimited.
(default 0)
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
store-out-of-bag-predictions
flag
Whether to store out of bag predictions in internal evaluation object.
Verified_Supervised_Classification
weka
weka_3.8.0
Verified_Supervised_Classification
weka
weka_3.8.0
https://api.openml.org/data/download/4628813/weka.classifiers.meta.FilteredClassifier4909737977157385085.class
class
f71415ba2ba368e6ad3957aa43154914