Visibility: public Uploaded 29-03-2018 by Gijs van der Sanden Weka_3.8.2 229 runs
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Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.


-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
BBreak ties randomly when several attributes look equally good.
INumber of iterations. (current value 100)default: 100
KNumber of attributes to randomly investigate. (default 0) (<1 = int(log_2(#predictors)+1)).default: 0
MSet minimum number of instances per leaf. (default 1)default: 1.0
NNumber of folds for backfitting (default 0, no backfitting).
OCalculate the out of bag error.
PSize of each bag, as a percentage of the training set size. (default 100)default: 100
SSeed for random number generator. (default 1)default: 1
UAllow unclassified instances.
VSet minimum numeric class variance proportion of train variance for split (default 1e-3).default: 0.001
attribute-importanceCompute and output attribute importance (mean impurity decrease method)
batch-sizeThe desired batch size for batch prediction (default 100).
depthThe maximum depth of the tree, 0 for unlimited. (default 0)
num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).
num-slotsNumber of execution slots. (default 1 - i.e. no parallelism) (use 0 to auto-detect number of cores)default: 1
output-debug-infoIf set, classifier is run in debug mode and may output additional info to the console
output-out-of-bag-complexity-statisticsWhether to output complexity-based statistics when out-of-bag evaluation is performed.
printPrint the individual classifiers in the output
store-out-of-bag-predictionsWhether to store out of bag predictions in internal evaluation object.


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