Task
Supervised Data Stream Classification on BNG(anneal,1000,1)

Supervised Data Stream Classification on BNG(anneal,1000,1)

Task 7329 Supervised Data Stream Classification BNG(anneal,1000,1) 9 runs submitted
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Jan van Rijn 10 years ago
ran flow moa.LeveragingBag_kNN(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,1)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9646, f_measure: 0.8785, kappa: 0.6812, kb_relative_information_score: 878327.5315, mean_absolute_error: 0.0446, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.8988, predictive_accuracy: 0.8931, prior_entropy: 2.585, recall: 0.8931, relative_absolute_error: 0.1604, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.159, root_relative_squared_error: 0.4266,
Jan van Rijn 10 years ago
ran flow moa.OzaBoostAdwin_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,1)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9848, f_measure: 0.9347, kappa: 0.8402, kb_relative_information_score: 841471.0101, mean_absolute_error: 0.0711, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.9344, predictive_accuracy: 0.9382, prior_entropy: 2.585, recall: 0.9382, relative_absolute_error: 0.2561, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.1488, root_relative_squared_error: 0.3993,
Jan van Rijn 10 years ago
ran flow moa.RuleClassifier(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,1)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8505, f_measure: 0.7804, kappa: 0.4219, kb_relative_information_score: 748491.0777, mean_absolute_error: 0.0906, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.7761, predictive_accuracy: 0.8022, prior_entropy: 2.585, recall: 0.8022, relative_absolute_error: 0.3261, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.2218, root_relative_squared_error: 0.5953,
Jan van Rijn 10 years ago
ran flow moa.OzaBoost_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,1)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9838, f_measure: 0.9405, kappa: 0.8527, kb_relative_information_score: 827340.3474, mean_absolute_error: 0.0783, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.9396, predictive_accuracy: 0.9418, prior_entropy: 2.585, recall: 0.9418, relative_absolute_error: 0.282, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.1524, root_relative_squared_error: 0.4088,
Jan van Rijn 10 years ago
ran flow moa.OzaBag_HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,1)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.959, f_measure: 0.9066, kappa: 0.7657, kb_relative_information_score: 875112.8165, mean_absolute_error: 0.0478, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.9054, predictive_accuracy: 0.9091, prior_entropy: 2.585, recall: 0.9091, relative_absolute_error: 0.1721, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.1534, root_relative_squared_error: 0.4116,
Jan van Rijn 10 years ago
ran flow moa.kNN(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,1)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9638, f_measure: 0.8788, kappa: 0.6822, kb_relative_information_score: 878493.5088, mean_absolute_error: 0.0445, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.8988, predictive_accuracy: 0.8934, prior_entropy: 2.585, recall: 0.8934, relative_absolute_error: 0.1602, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.159, root_relative_squared_error: 0.4265,
Jan van Rijn 10 years ago
ran flow moa.HoeffdingTree(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,1)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9439, f_measure: 0.8962, kappa: 0.7381, kb_relative_information_score: 867357.6723, mean_absolute_error: 0.0475, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.8964, predictive_accuracy: 0.8967, prior_entropy: 2.585, recall: 0.8967, relative_absolute_error: 0.1709, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.1691, root_relative_squared_error: 0.4539,
Jan van Rijn 10 years ago
ran flow moa.SGD(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,1)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4999, f_measure: 0.0221, kappa: -0.0003, kb_relative_information_score: 17979.8949, mean_absolute_error: 0.2971, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.0123, predictive_accuracy: 0.1087, prior_entropy: 2.585, recall: 0.1087, relative_absolute_error: 1.0696, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.5451, root_relative_squared_error: 1.4626,
Jan van Rijn 10 years ago
ran flow moa.NaiveBayes(1) on task Supervised Data Stream Classification on data set BNG(anneal,1000,1)
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8745, f_measure: 0.7584, kappa: 0.4373, kb_relative_information_score: 699508.2136, mean_absolute_error: 0.1003, mean_prior_absolute_error: 0.2778, number_of_instances: 1000000, precision: 0.7972, predictive_accuracy: 0.7319, prior_entropy: 2.585, recall: 0.7319, relative_absolute_error: 0.3612, root_mean_prior_squared_error: 0.3727, root_mean_squared_error: 0.2557, root_relative_squared_error: 0.686,

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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,1000,1) (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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