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