2051 824 weka.LogitBoost_AttributeSelectedClassifier_CfsSubsetEval_BestFirst_J48 1 Weka_3.7.13_11958 J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University. 2015-12-03T12:04:48 English Weka_3.7.13 -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). E option 1 The number of threads to use for batch prediction, which should be >= size of thread pool. (default 1) H option 1.0 Shrinkage parameter. (default 1) I option 10 Number of iterations. (default 10) L option -1.7976931348623157E308 Threshold on the improvement of the likelihood. (default -Double.MAX_VALUE) O option 1 The size of the thread pool, for example, the number of cores in the CPU. (default 1) P option 100 Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up) Q flag Use resampling instead of reweighting for boosting. S option 1 Random number seed. (default 1) W baselearner weka.classifiers.meta.AttributeSelectedClassifier Full name of base classifier. (default: weka.classifiers.trees.DecisionStump) Z option 3.0 Z max threshold for responses. (default 3) 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 2052 824 weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_J48 5 Weka_3.7.13_11461 Weka implementation of AttributeSelectedClassifier 2015-12-03T12:04:48 English Weka_3.7.13 -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 baselearner 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. 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 baselearner 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.trees.J48 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. 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 1720 1 weka.J48 33 Weka_3.7.13_11194 Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA. 2015-10-30T12:49:47 English Weka_3.7.13 -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. 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 mythbusting mythbusting_1 study_15 study_20 study_34 study_73 Verified_Learning_Curve,Verified_Supervised_Classification weka weka_3.7.13 https://api.openml.org/data/download/1681027/weka.classifiers.trees.J481019998014146204943.class class 5bd92b1eff2acdcf46b59e1af99f0850 E 2049 824 weka.CfsSubsetEval 5 Weka_3.7.13_11852 M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand. 2015-12-03T12:02:12 English Weka_3.7.13 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. weka weka_3.7.13 S 2050 824 weka.BestFirst 4 Weka_3.7.13_10396 Weka implementation of BestFirst 2015-12-03T12:02:12 English Weka_3.7.13 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) weka weka_3.7.13 weka weka_3.7.13 weka weka_3.7.13 https://api.openml.org/data/download/1726316/weka.classifiers.meta.LogitBoost3212746527820931670.class class 125fe543b65a89a18a591a7239db5809