5214
2
weka.RandomForest
10
Weka_3.9.0_12645
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
2016-12-04T21:38:20
English
Weka_3.9.0
-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.
weka
weka_3.9.0