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Supervised Data Stream Classification on BNG(SPECT)

Supervised Data Stream Classification on BNG(SPECT)

Task 7283 Supervised Data Stream Classification BNG(SPECT) 313 runs submitted
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  • binstreams ecmlpkdd2015 streamensembles streams study_11 study_16
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Jan van Rijn 10 years ago
ran flow moa.FIMTDD(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.0719, kb_relative_information_score: -583160, mean_absolute_error: 0.7916, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.0434, predictive_accuracy: 0.2084, prior_entropy: 1, recall: 0.2084, relative_absolute_error: 1.5832, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.8897, root_relative_squared_error: 1.7794,
Jan van Rijn 10 years ago
ran flow moa.ASHoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8662, f_measure: 0.8378, kappa: 0.4846, kb_relative_information_score: 590061.4962, mean_absolute_error: 0.2168, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8364, predictive_accuracy: 0.8475, prior_entropy: 1, recall: 0.8475, relative_absolute_error: 0.4337, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3331, root_relative_squared_error: 0.6661,
Jan van Rijn 10 years ago
ran flow moa.RuleClassifier(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6686, f_measure: 0.7263, kappa: 0.0847, kb_relative_information_score: 461919.7128, mean_absolute_error: 0.2853, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.7407, predictive_accuracy: 0.7922, prior_entropy: 1, recall: 0.7922, relative_absolute_error: 0.5707, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.393, root_relative_squared_error: 0.786,
Jan van Rijn 10 years ago
ran flow moa.OzaBoostAdwin_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8118, f_measure: 0.8026, kappa: 0.3905, kb_relative_information_score: 436967.5202, mean_absolute_error: 0.3, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.7995, predictive_accuracy: 0.8066, prior_entropy: 1, recall: 0.8066, relative_absolute_error: 0.6, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3699, root_relative_squared_error: 0.7398,
Jan van Rijn 10 years ago
ran flow moa.OzaBoost_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.868, f_measure: 0.8307, kappa: 0.4783, kb_relative_information_score: 362071.7835, mean_absolute_error: 0.3412, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8284, predictive_accuracy: 0.8338, prior_entropy: 1, recall: 0.8338, relative_absolute_error: 0.6825, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3756, root_relative_squared_error: 0.7512,
Jan van Rijn 10 years ago
ran flow moa.OzaBagAdwin_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8755, f_measure: 0.8404, kappa: 0.4931, kb_relative_information_score: 589836.9465, mean_absolute_error: 0.2177, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8393, predictive_accuracy: 0.85, prior_entropy: 1, recall: 0.85, relative_absolute_error: 0.4355, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3284, root_relative_squared_error: 0.6569,
Jan van Rijn 10 years ago
ran flow moa.OzaBag_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8755, f_measure: 0.8404, kappa: 0.4931, kb_relative_information_score: 589836.9465, mean_absolute_error: 0.2177, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8393, predictive_accuracy: 0.85, prior_entropy: 1, recall: 0.85, relative_absolute_error: 0.4355, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3284, root_relative_squared_error: 0.6569,
Jan van Rijn 10 years ago
ran flow moa.LeveragingBag_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8827, f_measure: 0.8447, kappa: 0.5094, kb_relative_information_score: 602623.7135, mean_absolute_error: 0.2103, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8431, predictive_accuracy: 0.8527, prior_entropy: 1, recall: 0.8527, relative_absolute_error: 0.4205, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3245, root_relative_squared_error: 0.649,
Jan van Rijn 10 years ago
ran flow moa.LeveragingBag_kNN(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7962, f_measure: 0.7987, kappa: 0.366, kb_relative_information_score: 491669.2869, mean_absolute_error: 0.2679, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.7937, predictive_accuracy: 0.808, prior_entropy: 1, recall: 0.808, relative_absolute_error: 0.5357, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3664, root_relative_squared_error: 0.7329,
Jan van Rijn 10 years ago
ran flow moa.kNN(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.796, f_measure: 0.7988, kappa: 0.3677, kb_relative_information_score: 491632.5775, mean_absolute_error: 0.2679, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.7939, predictive_accuracy: 0.8076, prior_entropy: 1, recall: 0.8076, relative_absolute_error: 0.5357, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3665, root_relative_squared_error: 0.733,
Jan van Rijn 10 years ago
ran flow moa.HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8662, f_measure: 0.8378, kappa: 0.4846, kb_relative_information_score: 590061.4962, mean_absolute_error: 0.2168, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.8364, predictive_accuracy: 0.8475, prior_entropy: 1, recall: 0.8475, relative_absolute_error: 0.4337, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3331, root_relative_squared_error: 0.6661,
Jan van Rijn 10 years ago
ran flow moa.SGD(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.6995, kappa: -0, kb_relative_information_score: 583158, mean_absolute_error: 0.2084, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.6266, predictive_accuracy: 0.7916, prior_entropy: 1, recall: 0.7916, relative_absolute_error: 0.4168, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.4565, root_relative_squared_error: 0.9131,
Jan van Rijn 10 years ago
ran flow moa.NaiveBayes(1) on task Supervised Data Stream Classification on data set BNG(SPECT)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7726, f_measure: 0.7687, kappa: 0.3469, kb_relative_information_score: 475263.9415, mean_absolute_error: 0.2667, mean_prior_absolute_error: 0.5, number_of_instances: 1000000, precision: 0.7892, predictive_accuracy: 0.7563, prior_entropy: 1, recall: 0.7563, relative_absolute_error: 0.5334, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.4271, root_relative_squared_error: 0.8542,

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Leaderboard

Rank Name Top Score Entries Highest rank

Note: The leaderboard ignores resubmissions of previous solutions, as well as parameter variations that do not improve performance.

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(SPECT) (1)
target_feature OVERALL_DIAGNOSIS

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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