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OpenML
OpenML
Supervised Classification on visualizing_ethanol

Supervised Classification on visualizing_ethanol

Task 3809 Supervised Classification visualizing_ethanol 538 runs submitted
0 likes downloaded by 0 people , 0 total downloads 0 issues
Visibility: Public
  • mythbusting_1 study_1 study_107 study_123 study_15 study_20 study_41 under100k under1m
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538 Runs

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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5119, f_measure: 0.4884, kappa: -0.0238, kb_relative_information_score: -0.307, mean_absolute_error: 0.5, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4884, predictive_accuracy: 0.4886, prior_entropy: 0.9996, recall: 0.4886, relative_absolute_error: 1.0005, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5826, root_relative_squared_error: 1.1655, scimark_benchmark: 1304.9611,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.508, f_measure: 0.4299, kappa: 0.0158, kb_relative_information_score: -0.0642, mean_absolute_error: 0.5, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5164, predictive_accuracy: 0.5, prior_entropy: 0.9996, recall: 0.5, relative_absolute_error: 1.0005, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.7071, root_relative_squared_error: 1.4146, scimark_benchmark: 1304.9611,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5349, f_measure: 0.4178, kappa: 0.0712, kb_relative_information_score: 7.9415, mean_absolute_error: 0.4545, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.7594, predictive_accuracy: 0.5455, prior_entropy: 0.9996, recall: 0.5455, relative_absolute_error: 0.9096, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.6742, root_relative_squared_error: 1.3487, scimark_benchmark: 1336.3256, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5129, f_measure: 0.5091, kappa: 0.0257, kb_relative_information_score: 1.9373, mean_absolute_error: 0.4886, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5135, predictive_accuracy: 0.5114, prior_entropy: 0.9996, recall: 0.5114, relative_absolute_error: 0.9778, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.699, root_relative_squared_error: 1.3984, scimark_benchmark: 1250.8301,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5364, f_measure: 0.5114, kappa: 0.0227, kb_relative_information_score: 1.612, mean_absolute_error: 0.4921, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5116, predictive_accuracy: 0.5114, prior_entropy: 0.9996, recall: 0.5114, relative_absolute_error: 0.9846, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5032, root_relative_squared_error: 1.0066, scimark_benchmark: 887.6719, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5114, f_measure: 0.5114, kappa: 0.0227, kb_relative_information_score: 1.9373, mean_absolute_error: 0.4886, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5116, predictive_accuracy: 0.5114, prior_entropy: 0.9996, recall: 0.5114, relative_absolute_error: 0.9778, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.699, root_relative_squared_error: 1.3984, scimark_benchmark: 938.343, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5566, f_measure: 0.5566, kappa: 0.1127, kb_relative_information_score: 2.403, mean_absolute_error: 0.489, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5566, predictive_accuracy: 0.5568, prior_entropy: 0.9996, recall: 0.5568, relative_absolute_error: 0.9786, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5, root_relative_squared_error: 1.0003, scimark_benchmark: 933.3136, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4532, f_measure: 0.4528, kappa: -0.0937, kb_relative_information_score: -8.0699, mean_absolute_error: 0.5455, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.453, predictive_accuracy: 0.4545, prior_entropy: 0.9996, recall: 0.4545, relative_absolute_error: 1.0915, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.7385, root_relative_squared_error: 1.4775, scimark_benchmark: 934.0566,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4486, f_measure: 0.346, kb_relative_information_score: -0.1356, mean_absolute_error: 0.5003, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.2615, predictive_accuracy: 0.5114, prior_entropy: 0.9996, recall: 0.5114, relative_absolute_error: 1.0011, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5004, root_relative_squared_error: 1.0011, scimark_benchmark: 1350.9691,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4486, f_measure: 0.346, kb_relative_information_score: -0.1356, mean_absolute_error: 0.5003, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.2615, predictive_accuracy: 0.5114, prior_entropy: 0.9996, recall: 0.5114, relative_absolute_error: 1.0011, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5004, root_relative_squared_error: 1.0011, scimark_benchmark: 935.4444,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4486, f_measure: 0.346, kb_relative_information_score: -0.1356, mean_absolute_error: 0.5003, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.2615, predictive_accuracy: 0.5114, prior_entropy: 0.9996, recall: 0.5114, relative_absolute_error: 1.0011, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5004, root_relative_squared_error: 1.0011, scimark_benchmark: 909.3537,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4044, f_measure: 0.4098, kappa: -0.1464, kb_relative_information_score: -4.578, mean_absolute_error: 0.5211, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4157, predictive_accuracy: 0.4318, prior_entropy: 0.9996, recall: 0.4318, relative_absolute_error: 1.0427, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5313, root_relative_squared_error: 1.0629, scimark_benchmark: 932.0534, usercpu_time_millis: 50, usercpu_time_millis_training: 50,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5835, f_measure: 0.5794, kappa: 0.16, kb_relative_information_score: 8.4687, mean_absolute_error: 0.4552, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5806, predictive_accuracy: 0.5795, prior_entropy: 0.9996, recall: 0.5795, relative_absolute_error: 0.9109, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5602, root_relative_squared_error: 1.1206, scimark_benchmark: 1315.0767, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5902, f_measure: 0.5905, kappa: 0.1805, kb_relative_information_score: 15.9473, mean_absolute_error: 0.4091, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5907, predictive_accuracy: 0.5909, prior_entropy: 0.9996, recall: 0.5909, relative_absolute_error: 0.8186, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.6396, root_relative_squared_error: 1.2795, scimark_benchmark: 1315.0767,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4341, f_measure: 0.4876, kappa: -0.0206, kb_relative_information_score: -4.1278, mean_absolute_error: 0.5218, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4898, predictive_accuracy: 0.4886, prior_entropy: 0.9996, recall: 0.4886, relative_absolute_error: 1.0441, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5788, root_relative_squared_error: 1.1579, scimark_benchmark: 1073.494,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4522, f_measure: 0.4488, kappa: -0.096, kb_relative_information_score: -2.4051, mean_absolute_error: 0.5096, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4506, predictive_accuracy: 0.4545, prior_entropy: 0.9996, recall: 0.4545, relative_absolute_error: 1.0198, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.549, root_relative_squared_error: 1.0983, scimark_benchmark: 1327.6929,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4997, f_measure: 0.5, kappa: -0.0005, kb_relative_information_score: -0.0642, mean_absolute_error: 0.5, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5, predictive_accuracy: 0.5, prior_entropy: 0.9996, recall: 0.5, relative_absolute_error: 1.0005, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.7071, root_relative_squared_error: 1.4146, scimark_benchmark: 1372.2145,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.3208, kb_relative_information_score: -2.0656, mean_absolute_error: 0.5114, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.2388, predictive_accuracy: 0.4886, prior_entropy: 0.9996, recall: 0.4886, relative_absolute_error: 1.0232, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.7151, root_relative_squared_error: 1.4306, scimark_benchmark: 1442.7264,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4475, f_measure: 0.4558, kappa: -0.0748, kb_relative_information_score: -3.2171, mean_absolute_error: 0.5155, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4603, predictive_accuracy: 0.4659, prior_entropy: 0.9996, recall: 0.4659, relative_absolute_error: 1.0314, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.537, root_relative_squared_error: 1.0743, scimark_benchmark: 1358.4523,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5214, f_measure: 0.5212, kappa: 0.043, kb_relative_information_score: 3.9387, mean_absolute_error: 0.4773, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5219, predictive_accuracy: 0.5227, prior_entropy: 0.9996, recall: 0.5227, relative_absolute_error: 0.955, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.6908, root_relative_squared_error: 1.3821, scimark_benchmark: 1351.788,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5452, f_measure: 0.5455, kappa: 0.0904, kb_relative_information_score: 7.9415, mean_absolute_error: 0.4545, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5455, predictive_accuracy: 0.5455, prior_entropy: 0.9996, recall: 0.5455, relative_absolute_error: 0.9096, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.6742, root_relative_squared_error: 1.3487, scimark_benchmark: 1353.5686,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4486, f_measure: 0.346, kb_relative_information_score: -0.1348, mean_absolute_error: 0.5003, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.2615, predictive_accuracy: 0.5114, prior_entropy: 0.9996, recall: 0.5114, relative_absolute_error: 1.0011, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5004, root_relative_squared_error: 1.0011, scimark_benchmark: 1503.3362,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.3873, f_measure: 0.4148, kappa: -0.1452, kb_relative_information_score: -7.0204, mean_absolute_error: 0.535, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4194, predictive_accuracy: 0.4318, prior_entropy: 0.9996, recall: 0.4318, relative_absolute_error: 1.0705, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.563, root_relative_squared_error: 1.1264, scimark_benchmark: 1384.4418,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5132, f_measure: 0.5104, kappa: 0.0207, kb_relative_information_score: 1.6365, mean_absolute_error: 0.4908, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5107, predictive_accuracy: 0.5114, prior_entropy: 0.9996, recall: 0.5114, relative_absolute_error: 0.982, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5588, root_relative_squared_error: 1.1179, scimark_benchmark: 927.0753,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5672, f_measure: 0.5909, kappa: 0.1814, kb_relative_information_score: 12.1094, mean_absolute_error: 0.4344, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5909, predictive_accuracy: 0.5909, prior_entropy: 0.9996, recall: 0.5909, relative_absolute_error: 0.8692, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5994, root_relative_squared_error: 1.1992, scimark_benchmark: 911.3823, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4925, build_cpu_time: 0.1168, build_memory: 33689773, f_measure: 0.5455, kappa: 0.0904, kb_relative_information_score: 2.0354, mean_absolute_error: 0.4933, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5455, predictive_accuracy: 0.5455, prior_entropy: 0.9996, recall: 0.5455, relative_absolute_error: 0.9871, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5995, root_relative_squared_error: 1.1994, scimark_benchmark: 899.4388,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5098, build_cpu_time: 1.6353, build_memory: 231006190.3636, f_measure: 0.5342, kappa: 0.0682, kb_relative_information_score: 4.3481, mean_absolute_error: 0.4762, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5343, predictive_accuracy: 0.5341, prior_entropy: 0.9996, recall: 0.5341, relative_absolute_error: 0.9529, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.68, root_relative_squared_error: 1.3604, scimark_benchmark: 945.5885,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4682, build_cpu_time: 0.6436, build_memory: 1104540862.2727, f_measure: 0.4863, kappa: -0.0196, kb_relative_information_score: -2.9707, mean_absolute_error: 0.5178, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4902, predictive_accuracy: 0.4886, prior_entropy: 0.9996, recall: 0.4886, relative_absolute_error: 1.036, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.584, root_relative_squared_error: 1.1684, scimark_benchmark: 942.3919,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4682, build_cpu_time: 0.6643, build_memory: 1340789132.1818, f_measure: 0.4863, kappa: -0.0196, kb_relative_information_score: -2.9707, mean_absolute_error: 0.5178, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4902, predictive_accuracy: 0.4886, prior_entropy: 0.9996, recall: 0.4886, relative_absolute_error: 1.036, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.584, root_relative_squared_error: 1.1684, scimark_benchmark: 931.9671,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4682, build_cpu_time: 0.5884, build_memory: 916276502.9091, f_measure: 0.4863, kappa: -0.0196, kb_relative_information_score: -2.9707, mean_absolute_error: 0.5178, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4902, predictive_accuracy: 0.4886, prior_entropy: 0.9996, recall: 0.4886, relative_absolute_error: 1.036, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.584, root_relative_squared_error: 1.1684, scimark_benchmark: 945.5885,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4331, build_cpu_time: 0.5336, build_memory: 1601685031.7273, f_measure: 0.4863, kappa: -0.0196, kb_relative_information_score: -6.2443, mean_absolute_error: 0.5345, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.4902, predictive_accuracy: 0.4886, prior_entropy: 0.9996, recall: 0.4886, relative_absolute_error: 1.0695, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.5966, root_relative_squared_error: 1.1934, scimark_benchmark: 874.7324,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5584, build_cpu_time: 0.0304, build_memory: 233922643.9091, f_measure: 0.5909, kappa: 0.1814, kb_relative_information_score: 14.931, mean_absolute_error: 0.4168, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5909, predictive_accuracy: 0.5909, prior_entropy: 0.9996, recall: 0.5909, relative_absolute_error: 0.8341, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.623, root_relative_squared_error: 1.2462, scimark_benchmark: 944.4197,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5501, build_cpu_time: 0.0978, build_memory: 957629954.2727, f_measure: 0.556, kappa: 0.1118, kb_relative_information_score: 10.5652, mean_absolute_error: 0.4381, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5564, predictive_accuracy: 0.5568, prior_entropy: 0.9996, recall: 0.5568, relative_absolute_error: 0.8766, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.65, root_relative_squared_error: 1.3003, scimark_benchmark: 945.0532,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5488, build_cpu_time: 0.2326, build_memory: 362030796.6364, f_measure: 0.545, kappa: 0.0895, kb_relative_information_score: 6.8968, mean_absolute_error: 0.4611, mean_prior_absolute_error: 0.4997, number_of_instances: 88, precision: 0.5451, predictive_accuracy: 0.5455, prior_entropy: 0.9996, recall: 0.5455, relative_absolute_error: 0.9227, root_mean_prior_squared_error: 0.4999, root_mean_squared_error: 0.6739, root_relative_squared_error: 1.3481, scimark_benchmark: 939.6298,

Metric:

Timeline

Plotting contribution timeline

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

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

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)

How to submit runs

Using your favorite machine learning environment

Download this task directly in your environment and automatically upload your results

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From your own software

Use one of our APIs to download data from OpenML and upload your results

OpenML APIs