6143 2742 weka.FilteredClassifier_Bagging_IBk weka.classifiers.meta.FilteredClassifier 1 Weka_3.8.1_12647 Weka implementation of FilteredClassifier 2017-04-11T11:50:19 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.Bagging 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 6144 2742 weka.Bagging_IBk weka.classifiers.meta.Bagging 6 Weka_3.8.1_13191 Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140. 2017-04-11T11:50:19 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.lazy.IBk 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 5918 2735 weka.IBk weka.classifiers.lazy.IBk 11 Weka_3.8.1_10141 D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66. 2017-03-28T23:37:51 English Weka_3.8.1 -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). A flag true The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch). E flag Minimise mean squared error rather than mean absolute error when using -X option with numeric prediction. F flag Weight neighbours by 1 - their distance (use when k > 1) I flag Weight neighbours by the inverse of their distance (use when k > 1) K option 1 Number of nearest neighbours (k) used in classification. (Default = 1) W option 0 Maximum number of training instances maintained. Training instances are dropped FIFO. (Default = no window) X flag Select the number of nearest neighbours between 1 and the k value specified using hold-one-out evaluation on the training data (use when k > 1) 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/4691512/weka.classifiers.lazy.IBk6633667906490039877.class class dadc825a6c0e0b12bc812872258448e6 weka weka_3.8.1 Verified_Supervised_Classification weka weka_3.8.1