3287 939 weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_SGD 1 Weka_3.7.13_11461 Weka implementation of AttributeSelectedClassifier 2016-03-18T13:45:24 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 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. 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.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.functions.SGD 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 1944 818 weka.SGD 4 Weka_3.7.13_11468 Weka implementation of SGD 2015-11-30T23:10:25 English Weka_3.7.13 -do-not-check-capabilities flag If set, classifier capabilities are not checked before classifier is built (use with caution). C option 0.001 The epsilon threshold (epsilon-insenstive and Huber loss only, default = 1e-3) E option 500 The number of epochs to perform (batch learning only, default = 500) F option 0 Set the loss function to minimize. 0 = hinge loss (SVM), 1 = log loss (logistic regression), 2 = squared loss (regression), 3 = epsilon insensitive loss (regression), 4 = Huber loss (regression). (default = 0) L option 0.01 The learning rate. If normalization is turned off (as it is automatically for streaming data), then the default learning rate will need to be reduced (try 0.0001). (default = 0.01). M flag Don't replace missing values N flag Don't normalize the data R option 1.0E-4 The lambda regularization constant (default = 0.0001) S option 1 Random number seed. (default 1) 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 study_15 study_20 Verified_Learning_Curve,Verified_Supervised_Classification weka weka_3.7.13 https://api.openml.org/data/download/1687727/weka.classifiers.functions.SGD4950430537509506920.class class a8220c187f13fa59696a69da9418b199 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 study_15 study_20 Verified_Supervised_Classification weka weka_3.7.13 https://api.openml.org/data/download/1836553/weka.classifiers.meta.AttributeSelectedClassifier9218818895155849678.class class e33297e05015e5c2e3533a8e686b98eb