Visibility: public Uploaded 29-12-2019 by chimi wangmo Weka_3.9.4 57 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).default: ["false"]
BBreak ties randomly when several attributes look equally good.default: ["false"]
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).default: []
OCalculate the out of bag error.default: ["false"]
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.default: ["false"]
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)default: ["false"]
batch-sizeThe desired batch size for batch prediction (default 100).default: []
depthThe maximum depth of the tree, 0 for unlimited. (default 0)default: []
num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).default: []
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 consoledefault: ["false"]
output-out-of-bag-complexity-statisticsWhether to output complexity-based statistics when out-of-bag evaluation is performed.default: ["false"]
printPrint the individual classifiers in the outputdefault: ["false"]
store-out-of-bag-predictionsWhether to store out of bag predictions in internal evaluation object.default: ["false"]


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