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Supervised Classification on ionosphere

Supervised Classification on ionosphere

Task 57 Supervised Classification ionosphere 1206 runs submitted
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Jan van Rijn 11 years ago
ran flow weka.RandomForest(1) on task Supervised Classification on data set ionosphere
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9743, f_measure: 0.9427, kappa: 0.8749, kb_relative_information_score: 263.48, mean_absolute_error: 0.1254, mean_prior_absolute_error: 0.4604, number_of_instances: 351, precision: 0.943, predictive_accuracy: 0.943, prior_entropy: 0.9425, recall: 0.943, relative_absolute_error: 0.2722, root_mean_prior_squared_error: 0.4797, root_mean_squared_error: 0.2295, root_relative_squared_error: 0.4783, scimark_benchmark: 2015.3685,
Jan van Rijn 11 years ago
ran flow weka.RandomTree(1) on task Supervised Classification on data set ionosphere
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8625, f_measure: 0.8769, kappa: 0.7315, kb_relative_information_score: 254.1134, mean_absolute_error: 0.1225, mean_prior_absolute_error: 0.4604, number_of_instances: 351, precision: 0.8767, predictive_accuracy: 0.8775, prior_entropy: 0.9425, recall: 0.8775, relative_absolute_error: 0.2661, root_mean_prior_squared_error: 0.4797, root_mean_squared_error: 0.35, root_relative_squared_error: 0.7296, scimark_benchmark: 2021.317,
Jan van Rijn 11 years ago
ran flow weka.IBk(1) on task Supervised Classification on data set ionosphere
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.805, f_measure: 0.8537, kappa: 0.6757, kb_relative_information_score: 239.4398, mean_absolute_error: 0.1418, mean_prior_absolute_error: 0.4604, number_of_instances: 351, precision: 0.8706, predictive_accuracy: 0.8604, prior_entropy: 0.9425, recall: 0.8604, relative_absolute_error: 0.308, root_mean_prior_squared_error: 0.4797, root_mean_squared_error: 0.3725, root_relative_squared_error: 0.7765, scimark_benchmark: 2008.2758,
Jan van Rijn 11 years ago
ran flow weka.HoeffdingTree(1) on task Supervised Classification on data set ionosphere
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.925, f_measure: 0.8835, kappa: 0.7475, kb_relative_information_score: 252.1374, mean_absolute_error: 0.1264, mean_prior_absolute_error: 0.4604, number_of_instances: 351, precision: 0.8839, predictive_accuracy: 0.8832, prior_entropy: 0.9425, recall: 0.8832, relative_absolute_error: 0.2746, root_mean_prior_squared_error: 0.4797, root_mean_squared_error: 0.3352, root_relative_squared_error: 0.6988, scimark_benchmark: 2008.848,
Jan van Rijn 11 years ago
ran flow weka.OneR(1) on task Supervised Classification on data set ionosphere
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8029, f_measure: 0.8224, kappa: 0.6121, kb_relative_information_score: 211.4123, mean_absolute_error: 0.1766, mean_prior_absolute_error: 0.4604, number_of_instances: 351, precision: 0.8219, predictive_accuracy: 0.8234, prior_entropy: 0.9425, recall: 0.8234, relative_absolute_error: 0.3836, root_mean_prior_squared_error: 0.4797, root_mean_squared_error: 0.4203, root_relative_squared_error: 0.8761, scimark_benchmark: 2012.8389,
Jan van Rijn 11 years ago
ran flow weka.J48(1) on task Supervised Classification on data set ionosphere
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8858, f_measure: 0.8932, kappa: 0.7656, kb_relative_information_score: 260.9592, mean_absolute_error: 0.1173, mean_prior_absolute_error: 0.4604, number_of_instances: 351, precision: 0.8945, predictive_accuracy: 0.8946, prior_entropy: 0.9425, recall: 0.8946, relative_absolute_error: 0.2547, root_mean_prior_squared_error: 0.4797, root_mean_squared_error: 0.3145, root_relative_squared_error: 0.6555, scimark_benchmark: 1979.2312,

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Challenge

In supervised classification, you are given an input dataset in which instances are labeled with a certain class. The goal is to build a model that predicts the class for future unlabeled instances. The model is evaluated using a train-test procedure, e.g. cross-validation.

To make results by different users comparable, you are given the exact train-test folds to be used, and you need to return at least the predictions generated by your model for each of the test instances. OpenML will use these predictions to calculate a range of evaluation measures on the server.

You can also upload your own evaluation measures, provided that the code for doing so is available from the implementation used. For extremely large datasets, it may be infeasible to upload all predictions. In those cases, you need to compute and provide the evaluations yourself.

Optionally, you can upload the model trained on all the input data. There is no restriction on the file format, but please use a well-known format or PMML.

Given inputs

estimation_procedure 10-fold Crossvalidation
evaluation_measures predictive_accuracy
source_data ionosphere (1)
target_feature class

Expected outputs

evaluations A list of user-defined evaluations of the task as key-value pairs. KeyValue (optional)
model A file containing the model built on all the input data. File (optional)
predictions The desired output format Predictions (optional)

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