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Supervised Data Stream Classification on BNG(credit-a)

Supervised Data Stream Classification on BNG(credit-a)

Task 2154 Supervised Data Stream Classification BNG(credit-a) 306 runs submitted
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  • binstreams ecmlpkdd2015 streamensembles streams study_11 study_16
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Jan van Rijn 11 years ago
ran flow moa.WEKAClassifier_J48(1) on task Supervised Data Stream Classification on data set BNG(credit-a)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8768, f_measure: 0.8655, kappa: 0.7281, kb_relative_information_score: 654655.5182, mean_absolute_error: 0.1848, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8658, predictive_accuracy: 0.8654, prior_entropy: 1, ram_hours: 0, recall: 0.8654, relative_absolute_error: 0.3697, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3391, root_relative_squared_error: 0.6782, run_cpu_time: 11.54,
Jan van Rijn 11 years ago
ran flow moa.HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(credit-a)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9329, f_measure: 0.8778, kappa: 0.7526, kb_relative_information_score: 676065.2711, mean_absolute_error: 0.1727, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8778, predictive_accuracy: 0.8778, prior_entropy: 1, ram_hours: 0, recall: 0.8778, relative_absolute_error: 0.3453, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.309, root_relative_squared_error: 0.6179, run_cpu_time: 16.07,
Jan van Rijn 11 years ago
ran flow moa.RuleClassifier(1) on task Supervised Data Stream Classification on data set BNG(credit-a)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8799, f_measure: 0.8096, kappa: 0.618, kb_relative_information_score: 480260.8684, mean_absolute_error: 0.2752, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8147, predictive_accuracy: 0.809, prior_entropy: 1, ram_hours: 0.0394, recall: 0.809, relative_absolute_error: 0.5503, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3681, root_relative_squared_error: 0.7362, run_cpu_time: 808.4159,
Jan van Rijn 11 years ago
ran flow moa.kNN(1) on task Supervised Data Stream Classification on data set BNG(credit-a)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9172, f_measure: 0.8599, kappa: 0.7163, kb_relative_information_score: 610702.3163, mean_absolute_error: 0.2063, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8598, predictive_accuracy: 0.8599, prior_entropy: 1, ram_hours: 0, recall: 0.8599, relative_absolute_error: 0.4126, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3293, root_relative_squared_error: 0.6587, run_cpu_time: 255.1543,
Jan van Rijn 11 years ago
ran flow moa.FIMTDD(1) on task Supervised Data Stream Classification on data set BNG(credit-a)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.2751, kb_relative_information_score: -108016, mean_absolute_error: 0.554, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.1989, predictive_accuracy: 0.446, prior_entropy: 1, ram_hours: 0.0009, recall: 0.446, relative_absolute_error: 1.108, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.7443, root_relative_squared_error: 1.4886, run_cpu_time: 60.15,
Jan van Rijn 11 years ago
ran flow moa.NoChange(1) on task Supervised Data Stream Classification on data set BNG(credit-a)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4997, f_measure: 0.5056, kappa: -0.0005, kb_relative_information_score: 11138, mean_absolute_error: 0.4944, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.5056, predictive_accuracy: 0.5056, prior_entropy: 1, ram_hours: 0, recall: 0.5056, relative_absolute_error: 0.9889, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.7032, root_relative_squared_error: 1.4063, run_cpu_time: 5.53,

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Challenge

Given a dataset with a nominal target, various data samples of increasing size are defined. A model is build for each individual data sample; from this a learning curve can be drawn.

Given inputs

estimation_procedure Interleaved Test then Train
source_data BNG(credit-a) (1)
target_feature class

Expected outputs

evaluations A list of user-defined evaluations of the task as key-value pairs. KeyValue (optional)
predictions The desired output format Predictions (optional)

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