Task
Supervised Data Stream Classification on BNG(anneal,1000,10)

Supervised Data Stream Classification on BNG(anneal,1000,10)

Task 7331 Supervised Data Stream Classification BNG(anneal,1000,10) 9 runs submitted
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Jan van Rijn 10 years ago
ran flow moa.OzaBoostAdwin_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,10)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9459, f_measure: 0.8655, kappa: 0.6588, kb_relative_information_score: 774461.1201, mean_absolute_error: 0.0934, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.8643, predictive_accuracy: 0.8766, prior_entropy: 2.585, recall: 0.8766, relative_absolute_error: 0.3363, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.1886, root_relative_squared_error: 0.5061,
Jan van Rijn 10 years ago
ran flow moa.OzaBagAdwin_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,10)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9835, f_measure: 0.9302, kappa: 0.8263, kb_relative_information_score: 890516.7674, mean_absolute_error: 0.0452, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.9296, predictive_accuracy: 0.9329, prior_entropy: 2.585, recall: 0.9329, relative_absolute_error: 0.1627, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.1324, root_relative_squared_error: 0.3554,
Jan van Rijn 10 years ago
ran flow moa.OzaBoost_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,10)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8741, f_measure: 0.8028, kappa: 0.4836, kb_relative_information_score: 690009.5451, mean_absolute_error: 0.1227, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.7988, predictive_accuracy: 0.8226, prior_entropy: 2.585, recall: 0.8226, relative_absolute_error: 0.4417, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.2215, root_relative_squared_error: 0.5944,
Jan van Rijn 10 years ago
ran flow moa.LeveragingBag_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,10)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9822, f_measure: 0.9294, kappa: 0.8252, kb_relative_information_score: 899098.5835, mean_absolute_error: 0.0407, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.9288, predictive_accuracy: 0.9324, prior_entropy: 2.585, recall: 0.9324, relative_absolute_error: 0.1465, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.1313, root_relative_squared_error: 0.3523,
Jan van Rijn 10 years ago
ran flow moa.OzaBag_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,10)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8375, f_measure: 0.7775, kappa: 0.4054, kb_relative_information_score: 725735.8501, mean_absolute_error: 0.1021, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.7756, predictive_accuracy: 0.8063, prior_entropy: 2.585, recall: 0.8063, relative_absolute_error: 0.3674, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.2215, root_relative_squared_error: 0.5944,
Jan van Rijn 10 years ago
ran flow moa.kNN(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,10)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9562, f_measure: 0.8533, kappa: 0.6181, kb_relative_information_score: 852124.035, mean_absolute_error: 0.0542, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.8772, predictive_accuracy: 0.8746, prior_entropy: 2.585, recall: 0.8746, relative_absolute_error: 0.195, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.1715, root_relative_squared_error: 0.4603,
Jan van Rijn 10 years ago
ran flow moa.HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,10)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7847, f_measure: 0.7668, kappa: 0.3844, kb_relative_information_score: 714490.4312, mean_absolute_error: 0.1022, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.7597, predictive_accuracy: 0.7896, prior_entropy: 2.585, recall: 0.7896, relative_absolute_error: 0.3678, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.2372, root_relative_squared_error: 0.6364,
Jan van Rijn 10 years ago
ran flow moa.SGD(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,10)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4998, f_measure: 0.022, kappa: -0.0004, kb_relative_information_score: 18167.1933, mean_absolute_error: 0.2971, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.0123, predictive_accuracy: 0.1088, prior_entropy: 2.585, recall: 0.1088, relative_absolute_error: 1.0694, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.545, root_relative_squared_error: 1.4625,
Jan van Rijn 10 years ago
ran flow moa.NaiveBayes(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,10)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7183, f_measure: 0.6874, kappa: 0.1879, kb_relative_information_score: 618062.237, mean_absolute_error: 0.1337, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.6722, predictive_accuracy: 0.707, prior_entropy: 2.585, recall: 0.707, relative_absolute_error: 0.4814, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.2665, root_relative_squared_error: 0.715,

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    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(anneal,1000,10) (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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