6313 2742 weka.CostSensitiveClassifier_FilteredClassifier_SMO_PolyKernel weka.classifiers.meta.CostSensitiveClassifier 1 Weka_3.8.1_12180 Weka implementation of CostSensitiveClassifier 2017-04-17T17:03:13 English Weka_3.8.1 -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). C option File name of a cost matrix to use. If this is not supplied, a cost matrix will be loaded on demand. The name of the on-demand file is the relation name of the training data plus ".cost", and the path to the on-demand file is specified with the -N option. M flag Minimize expected misclassification cost. Default is to reweight training instances according to costs per class N option C:\Program Files\Weka-3-8 Name of a directory to search for cost files when loading costs on demand (default current directory). S option 1 Random number seed. (default 1) W baselearner weka.classifiers.meta.FilteredClassifier Full name of base classifier. (default: weka.classifiers.rules.ZeroR) batch-size option The desired batch size for batch prediction (default 100). cost-matrix option The cost matrix in Matlab single line format. 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 6183 2775 weka.FilteredClassifier_SMO_PolyKernel weka.classifiers.meta.FilteredClassifier 1 Weka_3.8.1_12647 Weka implementation of FilteredClassifier 2017-04-12T14:11:16 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.functions.SMO 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 5926 2735 weka.SMO_PolyKernel weka.classifiers.functions.SMO 15 Weka_3.8.1_12558 J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, 1998. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy (2001). Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation. 13(3):637-649. Trevor Hastie, Robert Tibshirani: Classification by Pairwise Coupling. In: Advances in Neural Information Processing Systems, 1998. 2017-03-29T00:39:19 English Weka_3.8.1 -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). C option 1.0 The complexity constant C. (default 1) E option The Exponent to use. (default: 1.0) K kernel weka.classifiers.functions.supportVector.PolyKernel The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel) L option 0.001 The tolerance parameter. (default 1.0e-3) M flag Fit calibration models to SVM outputs. N option 0 Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize) P option 1.0E-12 The epsilon for round-off error. (default 1.0e-12) R option Set the ridge in the log-likelihood. V option -1 The number of folds for the internal cross-validation. (default -1, use training data) W option 1 The random number seed. (default 1) batch-size option The desired batch size for batch prediction (default 100). calibrator flag true Full name of calibration model, followed by options. (default: "weka.classifiers.functions.Logistic") no-checks flag Turns off all checks - use with caution! Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0. (default: checks on) 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 K 5927 2735 weka.PolyKernel weka.classifiers.functions.supportVector.PolyKernel 11 Weka_3.8.1_12533 Weka implementation of PolyKernel 2017-03-29T00:39:19 English Weka_3.8.1 C option 250007 The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007) E option 1.0 The Exponent to use. (default: 1.0) L flag Use lower-order terms. (default: no) no-checks flag Turns off all checks - use with caution! (default: checks on) output-debug-info flag Enables debugging output (if available) to be printed. (default: off) weka weka_3.8.1 study_73 Verified_Supervised_Classification weka weka_3.8.1 https://api.openml.org/data/download/4691858/weka.classifiers.functions.SMO3463712247787693537.class class 9bd3e7b0336ebcf8546734ec2cedfe2e Verified_Supervised_Classification weka weka_3.8.1 https://api.openml.org/data/download/5031044/weka.classifiers.meta.FilteredClassifier5494920847734709921.class class d36a54655368c6b3970f0bd7a40dda83 Verified_Supervised_Classification weka weka_3.8.1 https://api.openml.org/data/download/5035621/weka.classifiers.meta.CostSensitiveClassifier2190300404400233041.class class 1bd6d627a37903549b97baa041c4d2c2