6906 1856 weka.FilteredClassifier_AttributeSelectedClassifier_Bagging_OneR weka.classifiers.meta.FilteredClassifier 1 Weka_3.8.1_12647 Weka implementation of FilteredClassifier 2017-07-05T00:54:51 English Weka_3.8.1 -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.meta.AttributeSelectedClassifier 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 6907 1856 weka.AttributeSelectedClassifier_Bagging_OneR weka.classifiers.meta.AttributeSelectedClassifier 2 Weka_3.8.1_11461 Weka implementation of AttributeSelectedClassifier 2017-07-05T00:54:51 English Weka_3.8.1 -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 debugging info. E option weka.attributeSelection.CfsSubsetEval -P 1 -E 1 Full class name of attribute evaluator, followed by its options. eg: "weka.attributeSelection.CfsSubsetEval -L" (default weka.attributeSelection.CfsSubsetEval) J flag Do not use MDL correction for info gain on numeric attributes. 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. P option The size of the thread pool, for example, the number of cores in the CPU. (default 1) Q option Seed for random data shuffling (default 1). R flag Use reduced error pruning. S option weka.attributeSelection.BestFirst -D 1 -N 5 Full class name of search method, followed by its options. eg: "weka.attributeSelection.BestFirst -D 1" (default weka.attributeSelection.BestFirst) U flag Use unpruned tree. W baselearner weka.classifiers.meta.Bagging Full name of base classifier. (default: weka.classifiers.trees.J48) Z flag Precompute the full correlation matrix at the outset, rather than compute correlations lazily (as needed) during the search. Use this in conjuction with parallel processing in order to speed up a backward search. 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 W 6822 1856 weka.Bagging_OneR weka.classifiers.meta.Bagging 14 Weka_3.8.1_13191 Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140. 2017-07-02T21:04:24 English Weka_3.8.1 -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). I option 10 Number of iterations. (current value 10) L option Maximum tree depth (default -1, no maximum) M option Set minimum number of instances per leaf (default 2). N option Number of folds for reduced error pruning (default 3). 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) R flag Spread initial count over all class values (i.e. don't use 1 per value) S option 1 Random number seed. (default 1) V option Set minimum numeric class variance proportion of train variance for split (default 1e-3). W baselearner weka.classifiers.rules.OneR Full name of base classifier. (default: weka.classifiers.trees.REPTree) 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). 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 represent-copies-using-weights flag Represent copies of instances using weights rather than explicitly. store-out-of-bag-predictions flag Whether to store out of bag predictions in internal evaluation object. W 5942 2773 weka.OneR weka.classifiers.rules.OneR 20 Weka_3.8.1_10153 R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91. 2017-03-30T15:15:17 English Weka_3.8.1 -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). B option 6 The minimum number of objects in a bucket (default: 6). 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 Verified_Supervised_Classification weka weka_3.8.1 https://api.openml.org/data/download/4699319/weka.classifiers.rules.OneR1511221561521817134.class class 3d3f29f94353759bf757b3060bee87e3 weka weka_3.8.1 weka weka_3.8.1 Verified_Supervised_Classification weka weka_3.8.1 https://api.openml.org/data/download/9246076/weka.classifiers.meta.FilteredClassifier2794947458946197592.class class 273f4ff71122cbc074274fd619c61411