957 265 weka.LogitBoost_RandomSubSpace_LinearRegression 1 Weka_3.7.12-SNAPSHOT_10965 J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University. 2014-12-07T18:43:25 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). 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.RandomSubSpace Full name of base classifier. (default: weka.classifiers.trees.DecisionStump) Z option 3.0 Z max threshold for responses. (default 3) output-debug-info flag If set, classifier is run in debug mode and may output additional info to the console W 958 265 weka.RandomSubSpace_LinearRegression 1 Weka_3.7.12-SNAPSHOT_10470 Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL http://citeseer.ist.psu.edu/ho98random.html. 2014-12-07T18:43:25 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). 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). P option 0.5 Size of each subspace: < 1: percentage of the number of attributes >=1: absolute number of attributes 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.functions.LinearRegression 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 686 1 weka.LinearRegression 4 Weka_3.7.12-SNAPSHOT_11128 Weka implementation of LinearRegression 2014-11-03T18:50:37 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). C flag Do not try to eliminate colinear attributes. R option 1.0E-8 Set ridge parameter (default 1.0e-8). S option 0 Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method) additional-stats flag Output additional statistics. minimal flag Conserve memory, don't keep dataset header and means/stdevs. Model cannot be printed out if this option is enabled. (default: keep data) output-debug-info flag If set, classifier is run in debug mode and may output additional info to the console Verified_Learning_Curve weka weka_3.7.12-SNAPSHOT https://api.openml.org/data/download/120998/weka.classifiers.functions.LinearRegression6350462760096144059.class class 9a3e00472ba5600eeef509c84bb09050 weka weka_3.7.12-SNAPSHOT weka weka_3.7.12-SNAPSHOT https://api.openml.org/data/download/268044/weka.classifiers.meta.LogitBoost7145717625213438165.class class 213560bbc87618ef618c61e25d09d2f4