Task
Supervised Classification on colic

Supervised Classification on colic

Task 27 Supervised Classification colic 754 runs submitted
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  • basic mythbusting mythbusting_1 study_1 study_107 study_15 study_20 study_41 study_73 under100k
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9042, f_measure: 0.8543, kappa: 0.6847, kb_relative_information_score: 173.353, mean_absolute_error: 0.2657, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.855, predictive_accuracy: 0.856, prior_entropy: 0.9509, recall: 0.856, relative_absolute_error: 0.5699, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3376, root_relative_squared_error: 0.6995, scimark_benchmark: 911.0808,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7572, f_measure: 0.7743, kappa: 0.5152, kb_relative_information_score: 184.4394, mean_absolute_error: 0.2255, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.7741, predictive_accuracy: 0.7745, prior_entropy: 0.9509, recall: 0.7745, relative_absolute_error: 0.4838, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4749, root_relative_squared_error: 0.9839, scimark_benchmark: 937.9866,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5517, f_measure: 0.5747, kappa: 0.1252, kb_relative_information_score: 93.8681, mean_absolute_error: 0.337, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.6992, predictive_accuracy: 0.663, prior_entropy: 0.9509, recall: 0.663, relative_absolute_error: 0.7228, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.5805, root_relative_squared_error: 1.2026, scimark_benchmark: 941.6487,
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: 946.1943,
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,
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: 946.9416,
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: 947.6347,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8111, f_measure: 0.8382, kappa: 0.6478, kb_relative_information_score: 239.6658, mean_absolute_error: 0.1576, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8436, 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: 945.9677,
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: 918.8146,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8613, f_measure: 0.8136, kappa: 0.5973, kb_relative_information_score: 157.4541, mean_absolute_error: 0.2774, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8133, predictive_accuracy: 0.8152, prior_entropy: 0.9509, recall: 0.8152, relative_absolute_error: 0.5951, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.371, root_relative_squared_error: 0.7687, scimark_benchmark: 951.4421,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8176, f_measure: 0.8452, kappa: 0.6623, kb_relative_information_score: 190.3459, mean_absolute_error: 0.2397, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8559, predictive_accuracy: 0.8505, prior_entropy: 0.9509, recall: 0.8505, relative_absolute_error: 0.5143, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3552, root_relative_squared_error: 0.7359, scimark_benchmark: 944.9903,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8535, f_measure: 0.8087, kappa: 0.5843, kb_relative_information_score: 183.0079, mean_absolute_error: 0.2417, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8107, predictive_accuracy: 0.8125, prior_entropy: 0.9509, recall: 0.8125, relative_absolute_error: 0.5184, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3655, root_relative_squared_error: 0.7572, scimark_benchmark: 946.0438,
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: 911.5985,
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: 948.8995,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8434, f_measure: 0.8195, kappa: 0.6104, kb_relative_information_score: 173.2941, mean_absolute_error: 0.2543, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8191, predictive_accuracy: 0.8207, prior_entropy: 0.9509, recall: 0.8207, relative_absolute_error: 0.5455, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3824, root_relative_squared_error: 0.7922, scimark_benchmark: 947.0657,
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: 910.0676,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8088, f_measure: 0.8252, kappa: 0.6233, kb_relative_information_score: 226.4114, mean_absolute_error: 0.1739, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8248, predictive_accuracy: 0.8261, prior_entropy: 0.9509, recall: 0.8261, relative_absolute_error: 0.3731, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.417, root_relative_squared_error: 0.864, scimark_benchmark: 945.9238,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8895, f_measure: 0.8508, kappa: 0.6762, kb_relative_information_score: 196.3251, mean_absolute_error: 0.2315, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8528, predictive_accuracy: 0.8533, prior_entropy: 0.9509, recall: 0.8533, relative_absolute_error: 0.4965, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3413, root_relative_squared_error: 0.7071, scimark_benchmark: 949.5616,
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: 946.4312,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8148, f_measure: 0.8472, kappa: 0.6676, kb_relative_information_score: 189.2496, mean_absolute_error: 0.2426, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8513, predictive_accuracy: 0.8505, prior_entropy: 0.9509, recall: 0.8505, relative_absolute_error: 0.5204, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3534, root_relative_squared_error: 0.7322, scimark_benchmark: 910.7492,
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: 929.9524,
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: 946.4216,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8413, f_measure: 0.815, kappa: 0.5967, kb_relative_information_score: 190.6368, mean_absolute_error: 0.2238, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8217, predictive_accuracy: 0.8207, prior_entropy: 0.9509, recall: 0.8207, relative_absolute_error: 0.4801, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.3846, root_relative_squared_error: 0.7968, scimark_benchmark: 946.324,
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: 900.5798,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7527, f_measure: 0.7936, kappa: 0.5496, kb_relative_information_score: 210.9481, mean_absolute_error: 0.1929, mean_prior_absolute_error: 0.4662, number_of_instances: 368, precision: 0.8228, predictive_accuracy: 0.8071, prior_entropy: 0.9509, recall: 0.8071, relative_absolute_error: 0.4139, root_mean_prior_squared_error: 0.4827, root_mean_squared_error: 0.4392, root_relative_squared_error: 0.91, scimark_benchmark: 924.0005,
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: 924.9314,
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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: 0.8373, 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: 0.7675, 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: 0.8304, 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: 0.8191, 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: 0.8139, 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: 0.8288, 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: 0.8595, 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: 0.7686, 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: 0.8384, 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: 0.8895, 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: 0.7905, 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: 0.4878, 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: 0.8773, 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,

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