835 266 weka.AttributeSelectedClassifier_WrapperSubsetEval_ZeroR_GreedyStepwise_Bagging_NaiveBayes 1 Weka_3.7.12-SNAPSHOT_10141 Weka implementation of AttributeSelectedClassifier 2014-12-02T14:54:10 English Weka_3.7.12-SNAPSHOT -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 kernel weka.attributeSelection.WrapperSubsetEval 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 kernel weka.attributeSelection.GreedyStepwise 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. output-debug-info flag If set, classifier is run in debug mode and may output additional info to the console W 530 2 weka.Bagging_NaiveBayes 2 Weka_3.7.12-SNAPSHOT_10470 Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140. 2014-08-18T17:39:52 English Weka_3.7.12-SNAPSHOT -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). -represent-copies-using-weights flag Represent copies of instances using weights rather than explicitly. I option 10 Number of iterations. (default 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.bayes.NaiveBayes Full name of base classifier. (default: weka.classifiers.trees.REPTree) 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 W 380 1 weka.NaiveBayes 2 Weka_3.7.12-SNAPSHOT_10203 George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995. 2014-08-16T20:13:11 English Weka_3.7.12-SNAPSHOT -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). D flag Use supervised discretization to process numeric attributes K flag Use kernel density estimator rather than normal distribution for numeric attributes O flag Display model in old format (good when there are many classes) output-debug-info flag If set, classifier is run in debug mode and may output additional info to the console study_2 study_37 study_73 Verified_Learning_Curve,Verified_Supervised_Classification https://api.openml.org/data/download/49847/weka.classifiers.bayes.NaiveBayes5738087145074490555.class class 20dad3624d9becf0941822c151fd94cf study_2 study_37 Verified_Learning_Curve,Verified_Supervised_Classification https://api.openml.org/data/download/51564/weka.classifiers.meta.Bagging6907939767161094354.class class 3d52ba6b5b5403d48c0882b91eb51076 E 793 266 weka.WrapperSubsetEval_ZeroR 1 Weka_3.7.12-SNAPSHOT_11215 Ron Kohavi, George H. John (1997). Wrappers for feature subset selection. Artificial Intelligence. 97(1-2):273-324. 2014-12-01T12:57:41 English Weka_3.7.12-SNAPSHOT -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). B baselearner weka.classifiers.rules.ZeroR class name of base learner to use for accuracy estimation. Place any classifier options LAST on the command line following a "--". eg.: -B weka.classifiers.bayes.NaiveBayes ... -- -K (default: weka.classifiers.rules.ZeroR) E option acc Performance evaluation measure to use for selecting attributes. (Default = accuracy for discrete class and rmse for numeric class) F option 5 number of cross validation folds to use for estimating accuracy. (default=5) IRclass option Optional class value (label or 1-based index) to use in conjunction with IR statistics (f-meas, auc or auprc). Omitting this option will use the class-weighted average. R option 1 Seed for cross validation accuracy testimation. (default = 1) T option 0.01 threshold by which to execute another cross validation (standard deviation---expressed as a percentage of the mean). (default: 0.01 (1%)) output-debug-info flag If set, classifier is run in debug mode and may output additional info to the console B 364 1 weka.ZeroR 2 Weka_3.7.12-SNAPSHOT_10153 Weka implementation of ZeroR 2014-08-10T22:12:05 English Weka_3.7.12-SNAPSHOT -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). output-debug-info flag If set, classifier is run in debug mode and may output additional info to the console study_2 study_37 study_73 Verified_Learning_Curve,Verified_Supervised_Classification,Verified_Supervised_Regression https://api.openml.org/data/download/49668/weka.classifiers.rules.ZeroR5729400676178878788.class class 4498a30b6cfb13a39871f82552a445a4 weka weka_3.7.12-SNAPSHOT S 836 266 weka.GreedyStepwise 1 Weka_3.7.12-SNAPSHOT_11227 Weka implementation of GreedyStepwise 2014-12-02T14:54:10 English Weka_3.7.12-SNAPSHOT -B flag Use a backward search instead of a forward one. -C flag Use conservative forward search -num-slots option The number of execution slots, for example, the number of cores in the CPU. (default 1) D flag Print debugging output N option -1 Specify number of attributes to select P option Specify a starting set of attributes. Eg. 1,3,5-7. R flag Produce a ranked list of attributes. T option -1.7976931348623157E308 Specify a theshold by which attributes may be discarded from the ranking. Use in conjuction with -R weka weka_3.7.12-SNAPSHOT Verified_Supervised_Classification weka weka_3.7.12-SNAPSHOT https://api.openml.org/data/download/249862/weka.classifiers.meta.AttributeSelectedClassifier8743138117756474463.class class 254d1001fe710ef415a9c1bcaeaa7c51