1163 225 weka.FilteredClassifier_PrincipalComponents_J48 1 Weka_3.7.12_11142 Weka implementation of FilteredClassifier 2015-02-16T18:44:11 English Weka_3.7.12 -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 kernel weka.filters.unsupervised.attribute.PrincipalComponents 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.J48 Full name of base classifier. (default: weka.classifiers.trees.J48) Y flag Use bin numbers rather than ranges for discretized attributes. 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 1068 1 weka.J48 28 Weka_3.7.12_11194 Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA. 2015-02-05T16:06:52 English Weka_3.7.12 -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 0.25 Set confidence threshold for pruning. (default 0.25) 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 2 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. output-debug-info flag If set, classifier is run in debug mode and may output additional info to the console study_1 study_41 study_73 Verified_Learning_Curve,Verified_Supervised_Classification weka weka_3.7.12 https://api.openml.org/data/download/483041/weka.classifiers.trees.J481794474309767094055.class class 5bd92b1eff2acdcf46b59e1af99f0850 F 1164 225 weka.PrincipalComponents 12 Weka_3.7.12_10215 Weka implementation of PrincipalComponents 2015-02-16T18:44:11 English Weka_3.7.12 A option 5 Maximum number of attributes to include in transformed attribute names. (-1 = include all, default: 5) C flag Center (rather than standardize) the data and compute PCA using the covariance (rather than the correlation) matrix. M option -1 Maximum number of PC attributes to retain. (-1 = include all, default: -1) R option 0.95 Retain enough PC attributes to account for this proportion of variance in the original data. (default: 0.95) weka weka_3.7.12 study_1 study_41 Verified_Learning_Curve,Verified_Supervised_Classification weka weka_3.7.12 https://api.openml.org/data/download/533690/weka.classifiers.meta.FilteredClassifier5391861402654827492.class class 926b06b5048916807784bd1bda4434ef