5237 1995 weka.FilteredClassifier_RandomForest 3 Weka_3.8.0_12647 Weka implementation of FilteredClassifier 2016-12-04T22:53:40 English Weka_3.8.0 -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). -doNotMakeSplitPointActualValue flag Do not make split point actual value. A flag Laplace smoothing for predicted probabilities. B flag Use binary splits only. C option Set confidence threshold for pruning. (default 0.25) D flag Output binary attributes for discretized attributes. E flag Use better encoding of split point for MDL. F option weka.filters.supervised.attribute.Discretize -R first-last -precision 6 Full class name of filter to use, followed by filter options. eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2" J flag Do not use MDL correction for info gain on numeric attributes. K flag Use Kononenko's MDL criterion. L flag Do not clean up after the tree has been built. M option Set minimum number of instances per leaf. (default 2) N option Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3) O flag Do not collapse tree. Q option Seed for random data shuffling (default 1). R flag Use reduced error pruning. S flag Do not perform subtree raising. U flag Use unpruned tree. V flag Invert matching sense of column indexes. W baselearner weka.classifiers.trees.RandomForest Full name of base classifier. (default: weka.classifiers.trees.J48) Y flag Use bin numbers rather than ranges for discretized attributes. batch-size option The desired batch size for batch prediction (default 100). num-decimal-places option The number of decimal places for the output of numbers in the model (default 2). output-debug-info flag If set, classifier is run in debug mode and may output additional info to the console precision option Precision for bin boundary labels. (default = 6 decimal places). W 5238 1995 weka.RandomForest 11 Weka_3.8.0_12645 Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32. 2016-12-04T22:53:40 English Weka_3.8.0 -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. Verified_Supervised_Classification weka weka_3.8.0 Verified_Supervised_Classification weka weka_3.8.0 https://api.openml.org/data/download/4628813/weka.classifiers.meta.FilteredClassifier4909737977157385085.class class f71415ba2ba368e6ad3957aa43154914