Task
Supervised Data Stream Classification on vehicleNorm

Supervised Data Stream Classification on vehicleNorm

Task 7316 Supervised Data Stream Classification vehicleNorm 306 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.OzaBag_HoeffdingTree(1) on task Supervised Data Stream Classification on data set vehicleNorm
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8917, f_measure: 0.8414, kappa: 0.6846, kb_relative_information_score: 55097.0247, mean_absolute_error: 0.2366, mean_prior_absolute_error: 0.5, number_of_instances: 98528, precision: 0.8508, predictive_accuracy: 0.8423, prior_entropy: 1, recall: 0.8423, relative_absolute_error: 0.4731, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3462, root_relative_squared_error: 0.6924,
Jan van Rijn 10 years ago
ran flow moa.LeveragingBag_HoeffdingTree(1) on task Supervised Data Stream Classification on data set vehicleNorm
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.907, f_measure: 0.8526, kappa: 0.7063, kb_relative_information_score: 57490.3686, mean_absolute_error: 0.2235, mean_prior_absolute_error: 0.5, number_of_instances: 98528, precision: 0.8588, predictive_accuracy: 0.8532, prior_entropy: 1, recall: 0.8532, relative_absolute_error: 0.447, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3336, root_relative_squared_error: 0.6672,
Jan van Rijn 10 years ago
ran flow moa.kNN(1) on task Supervised Data Stream Classification on data set vehicleNorm
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8066, f_measure: 0.6985, kappa: 0.425, kb_relative_information_score: 32377.0393, mean_absolute_error: 0.3478, mean_prior_absolute_error: 0.5, number_of_instances: 98528, precision: 0.7612, predictive_accuracy: 0.7125, prior_entropy: 1, recall: 0.7125, relative_absolute_error: 0.6956, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.4373, root_relative_squared_error: 0.8746,
Jan van Rijn 10 years ago
ran flow moa.HoeffdingTree(1) on task Supervised Data Stream Classification on data set vehicleNorm
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8327, f_measure: 0.8303, kappa: 0.6624, kb_relative_information_score: 54859.4443, mean_absolute_error: 0.2355, mean_prior_absolute_error: 0.5, number_of_instances: 98528, precision: 0.8379, predictive_accuracy: 0.8312, prior_entropy: 1, recall: 0.8312, relative_absolute_error: 0.471, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3806, root_relative_squared_error: 0.7612,
Jan van Rijn 10 years ago
ran flow moa.SGD(1) on task Supervised Data Stream Classification on data set vehicleNorm
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8414, f_measure: 0.8407, kappa: 0.6828, kb_relative_information_score: 67270, mean_absolute_error: 0.1586, mean_prior_absolute_error: 0.5, number_of_instances: 98528, precision: 0.8471, predictive_accuracy: 0.8414, prior_entropy: 1, recall: 0.8414, relative_absolute_error: 0.3173, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.3983, root_relative_squared_error: 0.7966,
Jan van Rijn 10 years ago
ran flow moa.NaiveBayes(1) on task Supervised Data Stream Classification on data set vehicleNorm
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8151, f_measure: 0.8031, kappa: 0.6144, kb_relative_information_score: 60531.3493, mean_absolute_error: 0.1928, mean_prior_absolute_error: 0.5, number_of_instances: 98528, precision: 0.8353, predictive_accuracy: 0.8072, prior_entropy: 1, recall: 0.8072, relative_absolute_error: 0.3857, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.4382, root_relative_squared_error: 0.8765,

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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 vehicleNorm (1)
target_feature Y

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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