Visibility: public Uploaded 08-06-2021 by Tan Zheng Weka_3.9.5 0 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 (i.e., the number of trees in the random forest). (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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