295 2 weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_A1DE 1 Weka_3.7.10_9186 Weka implementation of AttributeSelectedClassifier 2014-06-03T22:43:36 English Weka_3.7.10 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 If set, classifier is run in debug mode and may output additional info to the console E kernel weka.attributeSelection.CfsSubsetEval 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. Q option Seed for random data shuffling (default 1). R flag Use reduced error pruning. S kernel weka.attributeSelection.BestFirst 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.bayes.AveragedNDependenceEstimators.A1DE Full name of base classifier. (default: weka.classifiers.trees.J48) E 134 2 weka.CfsSubsetEval 1 Weka_3.7.10_9883 M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand. 2014-05-15T00:21:40 English Weka_3.7.10 D flag Output debugging info. E option 1 The number of threads to use, which should be >= size of thread pool. (default 1) L flag Don't include locally predictive attributes. M flag Treat missing values as a separate value. P option 1 The size of the thread pool, for example, the number of cores in the CPU. (default 1) 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. S 135 2 weka.BestFirst 1 Weka_3.7.10_8034 Weka implementation of BestFirst 2014-05-15T00:21:40 English Weka_3.7.10 D option 1 Direction of search. (default = 1). N option 5 Number of non-improving nodes to consider before terminating search. P option Specify a starting set of attributes. Eg. 1,3,5-7. S option Size of lookup cache for evaluated subsets. Expressed as a multiple of the number of attributes in the data set. (default = 1) W 225 2 weka.A1DE 1 Weka_3.7.10_5516 G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24. 2014-06-03T15:26:14 English Weka_3.7.10 D flag Output debugging information F option 1 Impose a frequency limit for superParents (default is 1) M option 1.0 Specify a weight to use with m-estimate (default is 1) S option Specify a critical value for specialization-generalilzation SR (default is 100) W flag Specify if to use weighted AODE Verified_Supervised_Classification https://api.openml.org/data/download/30531/weka.classifiers.bayes.AveragedNDependenceEstimators.A1DE8657113863766566408.class class 37425c3ff0e8e605aa11bbed5772d537 Verified_Supervised_Classification https://api.openml.org/data/download/30888/weka.classifiers.meta.AttributeSelectedClassifier8345149132676471865.class class ea027543ba0b8bdda558e1d11361196e