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OpenML
OpenML
Supervised Classification on fri_c0_100_10

Supervised Classification on fri_c0_100_10

Task 3673 Supervised Classification fri_c0_100_10 754 runs submitted
0 likes downloaded by 0 people , 0 total downloads 0 issues
Visibility: Public
  • mythbusting_1 study_1 study_107 study_15 study_20 study_41 study_7 under100k under1m
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754 Runs

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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7683, f_measure: 0.7469, kappa: 0.4877, kb_relative_information_score: 33.7582, mean_absolute_error: 0.3379, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7515, predictive_accuracy: 0.75, prior_entropy: 0.9931, recall: 0.75, relative_absolute_error: 0.6824, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4455, root_relative_squared_error: 0.8954, scimark_benchmark: 825.5282,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7808, f_measure: 0.7874, kappa: 0.5697, kb_relative_information_score: 57.3836, mean_absolute_error: 0.21, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7929, predictive_accuracy: 0.79, prior_entropy: 0.9931, recall: 0.79, relative_absolute_error: 0.4242, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4583, root_relative_squared_error: 0.9211, scimark_benchmark: 930.5999, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.3903, kb_relative_information_score: 8.7112, mean_absolute_error: 0.45, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.3025, predictive_accuracy: 0.55, prior_entropy: 0.9931, recall: 0.55, relative_absolute_error: 0.9089, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.6708, root_relative_squared_error: 1.3484, scimark_benchmark: 1333.5799,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8384, f_measure: 0.84, kappa: 0.6768, kb_relative_information_score: 67.5237, mean_absolute_error: 0.16, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.84, predictive_accuracy: 0.84, prior_entropy: 0.9931, recall: 0.84, relative_absolute_error: 0.3232, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4, root_relative_squared_error: 0.804, scimark_benchmark: 1297.6599, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9152, f_measure: 0.82, kappa: 0.6364, kb_relative_information_score: 46.4901, mean_absolute_error: 0.2835, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.82, predictive_accuracy: 0.82, prior_entropy: 0.9931, recall: 0.82, relative_absolute_error: 0.5726, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3504, root_relative_squared_error: 0.7043, scimark_benchmark: 1335.643, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.796, f_measure: 0.7995, kappa: 0.5943, kb_relative_information_score: 59.4116, mean_absolute_error: 0.2, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7997, predictive_accuracy: 0.8, prior_entropy: 0.9931, recall: 0.8, relative_absolute_error: 0.404, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4472, root_relative_squared_error: 0.8989, scimark_benchmark: 942.6843,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9051, f_measure: 0.8092, kappa: 0.6138, kb_relative_information_score: 51.4931, mean_absolute_error: 0.2519, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.81, predictive_accuracy: 0.81, prior_entropy: 0.9931, recall: 0.81, relative_absolute_error: 0.5087, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3594, root_relative_squared_error: 0.7225, scimark_benchmark: 912.7324, usercpu_time_millis: 50, usercpu_time_millis_testing: 10, usercpu_time_millis_training: 40,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4972, f_measure: 0.4971, kappa: -0.0183, kb_relative_information_score: -0.1827, mean_absolute_error: 0.4927, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.4959, predictive_accuracy: 0.5, prior_entropy: 0.9931, recall: 0.5, relative_absolute_error: 0.9951, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.543, root_relative_squared_error: 1.0914, scimark_benchmark: 1073.494,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8024, f_measure: 0.7235, kappa: 0.4421, kb_relative_information_score: 38.8591, mean_absolute_error: 0.3104, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7358, predictive_accuracy: 0.73, prior_entropy: 0.9931, recall: 0.73, relative_absolute_error: 0.627, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4514, root_relative_squared_error: 0.9074, scimark_benchmark: 1304.6687, usercpu_time_millis: 30, usercpu_time_millis_training: 30,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8988, f_measure: 0.8279, kappa: 0.6516, kb_relative_information_score: 38.7562, mean_absolute_error: 0.3246, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8343, predictive_accuracy: 0.83, prior_entropy: 0.9931, recall: 0.83, relative_absolute_error: 0.6556, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3724, root_relative_squared_error: 0.7486, scimark_benchmark: 1318.1432, usercpu_time_millis: 30, usercpu_time_millis_training: 30,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7404, f_measure: 0.7469, kappa: 0.4877, kb_relative_information_score: 49.2715, mean_absolute_error: 0.25, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7515, predictive_accuracy: 0.75, prior_entropy: 0.9931, recall: 0.75, relative_absolute_error: 0.505, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.5, root_relative_squared_error: 1.005, scimark_benchmark: 1280.6952,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7166, f_measure: 0.6776, kappa: 0.3542, kb_relative_information_score: 29.4163, mean_absolute_error: 0.3549, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.6993, predictive_accuracy: 0.69, prior_entropy: 0.9931, recall: 0.69, relative_absolute_error: 0.7168, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4729, root_relative_squared_error: 0.9505, scimark_benchmark: 1315.3881,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7352, f_measure: 0.7184, kappa: 0.4297, kb_relative_information_score: 39.2103, mean_absolute_error: 0.3064, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7191, predictive_accuracy: 0.72, prior_entropy: 0.9931, recall: 0.72, relative_absolute_error: 0.6189, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4845, root_relative_squared_error: 0.9739, scimark_benchmark: 1355.7054, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6717, f_measure: 0.6776, kappa: 0.3542, kb_relative_information_score: 37.1034, mean_absolute_error: 0.31, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.6993, predictive_accuracy: 0.69, prior_entropy: 0.9931, recall: 0.69, relative_absolute_error: 0.6261, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.5568, root_relative_squared_error: 1.1192, scimark_benchmark: 1291.6995,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5222, f_measure: 0.4337, kappa: 0.0487, kb_relative_information_score: 12.7672, mean_absolute_error: 0.43, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7587, predictive_accuracy: 0.57, prior_entropy: 0.9931, recall: 0.57, relative_absolute_error: 0.8685, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.6557, root_relative_squared_error: 1.3181, scimark_benchmark: 1337.7959,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7364, f_measure: 0.6805, kappa: 0.3638, kb_relative_information_score: 34.965, mean_absolute_error: 0.3211, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.6898, predictive_accuracy: 0.68, prior_entropy: 0.9931, recall: 0.68, relative_absolute_error: 0.6486, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.5397, root_relative_squared_error: 1.0849, scimark_benchmark: 1465.2979,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5404, f_measure: 0.5465, kappa: 0.0816, kb_relative_information_score: 8.7112, mean_absolute_error: 0.45, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.5458, predictive_accuracy: 0.55, prior_entropy: 0.9931, recall: 0.55, relative_absolute_error: 0.9089, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.6708, root_relative_squared_error: 1.3484, scimark_benchmark: 1351.788,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7343, f_measure: 0.7306, kappa: 0.4622, kb_relative_information_score: 45.2155, mean_absolute_error: 0.27, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7383, predictive_accuracy: 0.73, prior_entropy: 0.9931, recall: 0.73, relative_absolute_error: 0.5453, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.5196, root_relative_squared_error: 1.0445, scimark_benchmark: 1353.5686,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4495, f_measure: 0.3903, kb_relative_information_score: -0.1774, mean_absolute_error: 0.4957, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.3025, predictive_accuracy: 0.55, prior_entropy: 0.9931, recall: 0.55, relative_absolute_error: 1.0011, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4981, root_relative_squared_error: 1.0012, scimark_benchmark: 1503.3362,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7291, f_measure: 0.7297, kappa: 0.4534, kb_relative_information_score: 42.7025, mean_absolute_error: 0.2859, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7295, predictive_accuracy: 0.73, prior_entropy: 0.9931, recall: 0.73, relative_absolute_error: 0.5775, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.5036, root_relative_squared_error: 1.0123, scimark_benchmark: 1466.6185,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9135, f_measure: 0.8396, kappa: 0.6755, kb_relative_information_score: 68.2561, mean_absolute_error: 0.156, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8399, predictive_accuracy: 0.84, prior_entropy: 0.9931, recall: 0.84, relative_absolute_error: 0.3151, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3818, root_relative_squared_error: 0.7674, scimark_benchmark: 931.2336, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8446, f_measure: 0.8403, kappa: 0.6781, kb_relative_information_score: 66.025, mean_absolute_error: 0.1705, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8412, predictive_accuracy: 0.84, prior_entropy: 0.9931, recall: 0.84, relative_absolute_error: 0.3443, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3871, root_relative_squared_error: 0.778, scimark_benchmark: 941.7954, usercpu_time_millis: 50, usercpu_time_millis_testing: 10, usercpu_time_millis_training: 40,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8129, f_measure: 0.8403, kappa: 0.6781, kb_relative_information_score: 64.6122, mean_absolute_error: 0.1782, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8412, predictive_accuracy: 0.84, prior_entropy: 0.9931, recall: 0.84, relative_absolute_error: 0.3599, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3956, root_relative_squared_error: 0.7951, scimark_benchmark: 923.7642, usercpu_time_millis: 30, usercpu_time_millis_testing: 10, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8307, f_measure: 0.8304, kappa: 0.6586, kb_relative_information_score: 63.0368, mean_absolute_error: 0.1853, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8322, predictive_accuracy: 0.83, prior_entropy: 0.9931, recall: 0.83, relative_absolute_error: 0.3743, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4012, root_relative_squared_error: 0.8064, scimark_benchmark: 894.7455, usercpu_time_millis: 30, usercpu_time_millis_testing: 10, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8871, f_measure: 0.8494, kappa: 0.6951, kb_relative_information_score: 68.8136, mean_absolute_error: 0.1547, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8504, predictive_accuracy: 0.85, prior_entropy: 0.9931, recall: 0.85, relative_absolute_error: 0.3124, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3714, root_relative_squared_error: 0.7465, scimark_benchmark: 911.3823, usercpu_time_millis: 100, usercpu_time_millis_training: 100,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8903, f_measure: 0.8293, kappa: 0.6545, kb_relative_information_score: 65.2211, mean_absolute_error: 0.1725, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8302, predictive_accuracy: 0.83, prior_entropy: 0.9931, recall: 0.83, relative_absolute_error: 0.3484, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3869, root_relative_squared_error: 0.7777, scimark_benchmark: 936.7115, usercpu_time_millis: 40, usercpu_time_millis_training: 40,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8663, f_measure: 0.7795, kappa: 0.5538, kb_relative_information_score: 56.1608, mean_absolute_error: 0.2175, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7796, predictive_accuracy: 0.78, prior_entropy: 0.9931, recall: 0.78, relative_absolute_error: 0.4393, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4184, root_relative_squared_error: 0.8411, scimark_benchmark: 938.2848, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.839, f_measure: 0.7481, kappa: 0.4898, kb_relative_information_score: 46.753, mean_absolute_error: 0.2638, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.75, predictive_accuracy: 0.75, prior_entropy: 0.9931, recall: 0.75, relative_absolute_error: 0.5329, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4483, root_relative_squared_error: 0.901, scimark_benchmark: 933.8635, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8016, f_measure: 0.7103, kappa: 0.4153, kb_relative_information_score: 41.1651, mean_absolute_error: 0.2919, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.7107, predictive_accuracy: 0.71, prior_entropy: 0.9931, recall: 0.71, relative_absolute_error: 0.5895, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.4626, root_relative_squared_error: 0.9299, scimark_benchmark: 938.4285, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9234, f_measure: 0.8896, kappa: 0.7764, kb_relative_information_score: 74.719, mean_absolute_error: 0.1281, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8908, predictive_accuracy: 0.89, prior_entropy: 0.9931, recall: 0.89, relative_absolute_error: 0.2588, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3233, root_relative_squared_error: 0.6498, scimark_benchmark: 936.6206, usercpu_time_millis: 70, usercpu_time_millis_training: 70,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9156, f_measure: 0.8797, kappa: 0.7566, kb_relative_information_score: 70.6182, mean_absolute_error: 0.1505, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8801, predictive_accuracy: 0.88, prior_entropy: 0.9931, recall: 0.88, relative_absolute_error: 0.304, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3238, root_relative_squared_error: 0.6509, scimark_benchmark: 924.6116, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.899, f_measure: 0.8293, kappa: 0.6545, kb_relative_information_score: 63.2325, mean_absolute_error: 0.1865, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8302, predictive_accuracy: 0.83, prior_entropy: 0.9931, recall: 0.83, relative_absolute_error: 0.3767, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3525, root_relative_squared_error: 0.7085, scimark_benchmark: 945.6434, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8747, f_measure: 0.8196, kappa: 0.6349, kb_relative_information_score: 55.1313, mean_absolute_error: 0.2313, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.8198, predictive_accuracy: 0.82, prior_entropy: 0.9931, recall: 0.82, relative_absolute_error: 0.4672, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3768, root_relative_squared_error: 0.7575, scimark_benchmark: 947.9494,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8648, f_measure: 0.82, kappa: 0.6364, kb_relative_information_score: 49.0037, mean_absolute_error: 0.2659, mean_prior_absolute_error: 0.4951, number_of_instances: 100, precision: 0.82, predictive_accuracy: 0.82, prior_entropy: 0.9931, recall: 0.82, relative_absolute_error: 0.537, root_mean_prior_squared_error: 0.4975, root_mean_squared_error: 0.3782, root_relative_squared_error: 0.7602, scimark_benchmark: 929.566,
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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

OpenML bootcamp

From your own software

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

OpenML APIs

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