8692
1
weka.classifiers.trees.RandomForest
weka.classifiers.trees.RandomForest
1
Weka_3.4.3_13294
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
2018-08-17T19:48:24
English
Weka_3.4.3
-do-not-check-capabilities
flag
["false"]
If set, classifier capabilities are not checked before classifier is built
(use with caution).
B
flag
["false"]
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
["false"]
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
["false"]
Allow unclassified instances.
V
option
["0.001"]
Set minimum numeric class variance proportion
of train variance for split (default 1e-3).
attribute-importance
flag
["false"]
Compute and output attribute importance (mean impurity decrease method)
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
["false"]
If set, classifier is run in debug mode and
may output additional info to the console
output-out-of-bag-complexity-statistics
flag
["false"]
Whether to output complexity-based statistics when out-of-bag evaluation is performed.
print
flag
["false"]
Print the individual classifiers in the output
store-out-of-bag-predictions
flag
["false"]
Whether to store out of bag predictions in internal evaluation object.
weka
weka_3.4.3