Task
Supervised Classification on colic

Supervised Classification on colic

Task 27 Supervised Classification colic 754 runs submitted
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8773, f_measure: 0.8511, kappa: 0.6772, kb_relative_information_score: 196.5298, mean_absolute_error: 0.2306, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8525, predictive_accuracy: 0.8533, prior_entropy: 0.9509, recall: 0.8533, relative_absolute_error: 0.4946, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3481, root_relative_squared_error: 0.7212, scimark_benchmark: 1977.4715,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4878, f_measure: 0.4875, kb_relative_information_score: -0.1525, mean_absolute_error: 0.4662, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.3974, predictive_accuracy: 0.6304, prior_entropy: 0.9509, recall: 0.6304, relative_absolute_error: 1.0001, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4827, root_relative_squared_error: 1.0001, scimark_benchmark: 2019.4749,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7905, f_measure: 0.8498, kappa: 0.6732, kb_relative_information_score: 189.9933, mean_absolute_error: 0.2422, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8545, predictive_accuracy: 0.8533, prior_entropy: 0.9509, recall: 0.8533, relative_absolute_error: 0.5197, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3593, root_relative_squared_error: 0.7444, scimark_benchmark: 2019.4104,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8895, f_measure: 0.8459, kappa: 0.6663, kb_relative_information_score: 163.1205, mean_absolute_error: 0.2757, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8467, predictive_accuracy: 0.8478, prior_entropy: 0.9509, recall: 0.8478, relative_absolute_error: 0.5915, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3512, root_relative_squared_error: 0.7276, scimark_benchmark: 2011.0425,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8384, f_measure: 0.7953, kappa: 0.566, kb_relative_information_score: 189.0227, mean_absolute_error: 0.2224, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.7995, predictive_accuracy: 0.7935, prior_entropy: 0.9509, recall: 0.7935, relative_absolute_error: 0.477, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4168, root_relative_squared_error: 0.8635, scimark_benchmark: 2010.1056,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7686, f_measure: 0.8171, kappa: 0.6129, kb_relative_information_score: 151.6321, mean_absolute_error: 0.2903, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.822, predictive_accuracy: 0.8152, prior_entropy: 0.9509, recall: 0.8152, relative_absolute_error: 0.6229, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3822, root_relative_squared_error: 0.7919, scimark_benchmark: 2018.6184,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8595, f_measure: 0.8232, kappa: 0.6204, kb_relative_information_score: 168.4361, mean_absolute_error: 0.2623, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8231, predictive_accuracy: 0.8234, prior_entropy: 0.9509, recall: 0.8234, relative_absolute_error: 0.5627, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3747, root_relative_squared_error: 0.7762, scimark_benchmark: 2022.4024,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8288, f_measure: 0.8372, kappa: 0.6468, kb_relative_information_score: 190.4372, mean_absolute_error: 0.2373, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8386, predictive_accuracy: 0.8397, prior_entropy: 0.9509, recall: 0.8397, relative_absolute_error: 0.5091, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3668, root_relative_squared_error: 0.7599, scimark_benchmark: 2009.128,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8139, f_measure: 0.8171, kappa: 0.6129, kb_relative_information_score: 217.5752, mean_absolute_error: 0.1848, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.822, predictive_accuracy: 0.8152, prior_entropy: 0.9509, recall: 0.8152, relative_absolute_error: 0.3964, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4299, root_relative_squared_error: 0.8906, scimark_benchmark: 2014.0772,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8191, f_measure: 0.8464, kappa: 0.6655, kb_relative_information_score: 246.2929, mean_absolute_error: 0.1495, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8527, predictive_accuracy: 0.8505, prior_entropy: 0.9509, recall: 0.8505, relative_absolute_error: 0.3206, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3866, root_relative_squared_error: 0.8009, scimark_benchmark: 2018.5935,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8304, f_measure: 0.7866, kappa: 0.5395, kb_relative_information_score: 166.5146, mean_absolute_error: 0.258, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.786, predictive_accuracy: 0.788, prior_entropy: 0.9509, recall: 0.788, relative_absolute_error: 0.5534, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4032, root_relative_squared_error: 0.8353, scimark_benchmark: 2023.1985,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7675, f_measure: 0.7778, kappa: 0.5247, kb_relative_information_score: 185.7364, mean_absolute_error: 0.2245, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.7787, predictive_accuracy: 0.7772, prior_entropy: 0.9509, recall: 0.7772, relative_absolute_error: 0.4815, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4706, root_relative_squared_error: 0.975, scimark_benchmark: 2013.9864,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8373, f_measure: 0.7992, kappa: 0.5698, kb_relative_information_score: 180.0088, mean_absolute_error: 0.2407, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.7996, predictive_accuracy: 0.7989, prior_entropy: 0.9509, recall: 0.7989, relative_absolute_error: 0.5164, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3918, root_relative_squared_error: 0.8116, scimark_benchmark: 945.8322,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8373, f_measure: 0.7992, kappa: 0.5698, kb_relative_information_score: 180.0088, mean_absolute_error: 0.2407, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.7996, predictive_accuracy: 0.7989, prior_entropy: 0.9509, recall: 0.7989, relative_absolute_error: 0.5164, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3918, root_relative_squared_error: 0.8116, scimark_benchmark: 935.5988,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8405, f_measure: 0.8054, kappa: 0.5852, kb_relative_information_score: 192.8795, mean_absolute_error: 0.2199, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8073, predictive_accuracy: 0.8043, prior_entropy: 0.9509, recall: 0.8043, relative_absolute_error: 0.4718, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4092, root_relative_squared_error: 0.8477, scimark_benchmark: 925.1566,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8384, f_measure: 0.7953, kappa: 0.566, kb_relative_information_score: 189.0227, mean_absolute_error: 0.2224, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.7995, predictive_accuracy: 0.7935, prior_entropy: 0.9509, recall: 0.7935, relative_absolute_error: 0.477, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4168, root_relative_squared_error: 0.8635, scimark_benchmark: 945.8205,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8956, f_measure: 0.8476, kappa: 0.6724, kb_relative_information_score: 210.5131, mean_absolute_error: 0.2079, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8474, predictive_accuracy: 0.8478, prior_entropy: 0.9509, recall: 0.8478, relative_absolute_error: 0.446, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3378, root_relative_squared_error: 0.6998, scimark_benchmark: 947.5478,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8017, f_measure: 0.8494, kappa: 0.6748, kb_relative_information_score: 197.0585, mean_absolute_error: 0.2321, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8494, predictive_accuracy: 0.8505, prior_entropy: 0.9509, recall: 0.8505, relative_absolute_error: 0.4979, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.365, root_relative_squared_error: 0.7561, scimark_benchmark: 945.5319,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5551, f_measure: 0.5735, kappa: 0.1352, kb_relative_information_score: 100.4953, mean_absolute_error: 0.3288, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.7839, predictive_accuracy: 0.6712, prior_entropy: 0.9509, recall: 0.6712, relative_absolute_error: 0.7054, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.5734, root_relative_squared_error: 1.188, scimark_benchmark: 909.2037,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8553, f_measure: 0.8206, kappa: 0.6109, kb_relative_information_score: 186.7914, mean_absolute_error: 0.2336, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8217, predictive_accuracy: 0.8234, prior_entropy: 0.9509, recall: 0.8234, relative_absolute_error: 0.5011, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3711, root_relative_squared_error: 0.7689, scimark_benchmark: 895.0484,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8492, f_measure: 0.8275, kappa: 0.6275, kb_relative_information_score: 194.2376, mean_absolute_error: 0.2257, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8273, predictive_accuracy: 0.8288, prior_entropy: 0.9509, recall: 0.8288, relative_absolute_error: 0.4842, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3778, root_relative_squared_error: 0.7827, scimark_benchmark: 920.4822,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7894, f_measure: 0.8021, kappa: 0.5762, kb_relative_information_score: 206.5299, mean_absolute_error: 0.1984, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8026, predictive_accuracy: 0.8016, prior_entropy: 0.9509, recall: 0.8016, relative_absolute_error: 0.4255, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4454, root_relative_squared_error: 0.9227, scimark_benchmark: 909.8139,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8889, f_measure: 0.8453, kappa: 0.6642, kb_relative_information_score: 194.2376, mean_absolute_error: 0.2345, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8472, predictive_accuracy: 0.8478, prior_entropy: 0.9509, recall: 0.8478, relative_absolute_error: 0.503, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3413, root_relative_squared_error: 0.7072, scimark_benchmark: 916.4847,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8511, f_measure: 0.8198, kappa: 0.6084, kb_relative_information_score: 190.8145, mean_absolute_error: 0.2261, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8222, predictive_accuracy: 0.8234, prior_entropy: 0.9509, recall: 0.8234, relative_absolute_error: 0.4851, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3765, root_relative_squared_error: 0.78, scimark_benchmark: 932.1143,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.797, f_measure: 0.8283, kappa: 0.6255, kb_relative_information_score: 233.0386, mean_absolute_error: 0.1658, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.838, predictive_accuracy: 0.8342, prior_entropy: 0.9509, recall: 0.8342, relative_absolute_error: 0.3556, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4071, root_relative_squared_error: 0.8435, scimark_benchmark: 946.5938,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5199, f_measure: 0.5222, kappa: 0.0495, kb_relative_information_score: 78.4048, mean_absolute_error: 0.356, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.7202, predictive_accuracy: 0.644, prior_entropy: 0.9509, recall: 0.644, relative_absolute_error: 0.7636, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.5966, root_relative_squared_error: 1.2361, scimark_benchmark: 948.4093,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8123, f_measure: 0.855, kappa: 0.6843, kb_relative_information_score: 189.9308, mean_absolute_error: 0.2424, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.861, predictive_accuracy: 0.8587, prior_entropy: 0.9509, recall: 0.8587, relative_absolute_error: 0.52, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3508, root_relative_squared_error: 0.7267, scimark_benchmark: 948.0426,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8956, f_measure: 0.8476, kappa: 0.6724, kb_relative_information_score: 210.5131, mean_absolute_error: 0.2079, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8474, predictive_accuracy: 0.8478, prior_entropy: 0.9509, recall: 0.8478, relative_absolute_error: 0.446, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3378, root_relative_squared_error: 0.6998, scimark_benchmark: 948.6592,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8169, f_measure: 0.8557, kappa: 0.6863, kb_relative_information_score: 188.6824, mean_absolute_error: 0.2441, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8595, predictive_accuracy: 0.8587, prior_entropy: 0.9509, recall: 0.8587, relative_absolute_error: 0.5237, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3519, root_relative_squared_error: 0.7291, scimark_benchmark: 947.1169,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8123, f_measure: 0.855, kappa: 0.6843, kb_relative_information_score: 189.9308, mean_absolute_error: 0.2424, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.861, predictive_accuracy: 0.8587, prior_entropy: 0.9509, recall: 0.8587, relative_absolute_error: 0.52, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3508, root_relative_squared_error: 0.7267, scimark_benchmark: 911.1882,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7957, f_measure: 0.8498, kappa: 0.6732, kb_relative_information_score: 189.9933, mean_absolute_error: 0.2422, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8545, predictive_accuracy: 0.8533, prior_entropy: 0.9509, recall: 0.8533, relative_absolute_error: 0.5197, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3578, root_relative_squared_error: 0.7413, scimark_benchmark: 945.9001,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.4875, kb_relative_information_score: 67.3595, mean_absolute_error: 0.3696, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.3974, predictive_accuracy: 0.6304, prior_entropy: 0.9509, recall: 0.6304, relative_absolute_error: 0.7928, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.6079, root_relative_squared_error: 1.2594, scimark_benchmark: 938.5248,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7947, f_measure: 0.8498, kappa: 0.6732, kb_relative_information_score: 189.3906, mean_absolute_error: 0.2431, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8545, predictive_accuracy: 0.8533, prior_entropy: 0.9509, recall: 0.8533, relative_absolute_error: 0.5215, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3583, root_relative_squared_error: 0.7424, scimark_benchmark: 947.6588,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8537, f_measure: 0.8232, kappa: 0.6144, kb_relative_information_score: 181.1756, mean_absolute_error: 0.2447, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.831, predictive_accuracy: 0.8288, prior_entropy: 0.9509, recall: 0.8288, relative_absolute_error: 0.5249, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.365, root_relative_squared_error: 0.7561,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8571, f_measure: 0.8262, kappa: 0.6211, kb_relative_information_score: 177.4821, mean_absolute_error: 0.2507, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8335, predictive_accuracy: 0.8315, prior_entropy: 0.9509, recall: 0.8315, relative_absolute_error: 0.5378, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3647, root_relative_squared_error: 0.7555, scimark_benchmark: 900.1661,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8202, f_measure: 0.8404, kappa: 0.6544, kb_relative_information_score: 239.6658, mean_absolute_error: 0.1576, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8411, predictive_accuracy: 0.8424, prior_entropy: 0.9509, recall: 0.8424, relative_absolute_error: 0.3381, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.397, root_relative_squared_error: 0.8225, scimark_benchmark: 948.8278,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8135, f_measure: 0.8498, kappa: 0.6732, kb_relative_information_score: 189.3362, mean_absolute_error: 0.2429, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8545, predictive_accuracy: 0.8533, prior_entropy: 0.9509, recall: 0.8533, relative_absolute_error: 0.5211, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3531, root_relative_squared_error: 0.7315, scimark_benchmark: 946.0126,

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