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