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Supervised Data Stream Classification on BNG(kr-vs-kp)

Supervised Data Stream Classification on BNG(kr-vs-kp)

Task 122 Supervised Data Stream Classification BNG(kr-vs-kp) 303 runs submitted
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  • binstreams ecmlpkdd2015 streamensembles streams streams-full study_11 study_16
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Jan van Rijn 11 years ago
ran flow moa.NaiveBayes(1) on task Supervised Data Stream Classification on data set BNG(kr-vs-kp)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9243, f_measure: 0.8509, kappa: 0.7012, kb_relative_information_score: 556763.0082, mean_absolute_error: 0.2366, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.851, predictive_accuracy: 0.851, prior_entropy: 1, ram_hours: 0, recall: 0.851, relative_absolute_error: 0.4731, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3324, root_relative_squared_error: 0.6648, run_cpu_time: 11.42,
Jan van Rijn 11 years ago
ran flow moa.LeveragingBag_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(kr-vs-kp)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.989, f_measure: 0.9567, kappa: 0.9133, kb_relative_information_score: 875144.2249, mean_absolute_error: 0.0679, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.9574, predictive_accuracy: 0.9567, prior_entropy: 1, ram_hours: 0.01, recall: 0.9567, relative_absolute_error: 0.1357, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.1855, root_relative_squared_error: 0.371, run_cpu_time: 384.37,
Jan van Rijn 11 years ago
ran flow moa.LeveragingBag_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(kr-vs-kp)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.989, f_measure: 0.9567, kappa: 0.9133, kb_relative_information_score: 875144.2249, mean_absolute_error: 0.0679, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.9574, predictive_accuracy: 0.9567, prior_entropy: 1, ram_hours: 0.0097, recall: 0.9567, relative_absolute_error: 0.1357, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.1855, root_relative_squared_error: 0.371, run_cpu_time: 374.87,

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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(kr-vs-kp) (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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