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Function: require_once

OpenML
OpenML
Supervised Classification on hepatitis

Supervised Classification on hepatitis

Task 54 Supervised Classification hepatitis 834 runs submitted
0 likes downloaded by 0 people , 0 total downloads 0 issues
Visibility: Public
  • basic mythbusting mythbusting_1 study_1 study_107 study_15 study_20 study_41 study_50 under100k under1m
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834 Runs

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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7177, f_measure: 0.778, kappa: 0.3147, kb_relative_information_score: 24.5789, mean_absolute_error: 0.2433, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7756, predictive_accuracy: 0.7806, prior_entropy: 0.7418, recall: 0.7806, relative_absolute_error: 0.7377, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4347, root_relative_squared_error: 1.074, scimark_benchmark: 919.2365,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8169, f_measure: 0.8202, kappa: 0.4358, kb_relative_information_score: 54.1259, mean_absolute_error: 0.1847, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8167, predictive_accuracy: 0.8258, prior_entropy: 0.7418, recall: 0.8258, relative_absolute_error: 0.5599, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3968, root_relative_squared_error: 0.9803, scimark_benchmark: 949.5216,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.7022, kb_relative_information_score: 41.7587, mean_absolute_error: 0.2065, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6297, predictive_accuracy: 0.7935, prior_entropy: 0.7418, recall: 0.7935, relative_absolute_error: 0.6259, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4544, root_relative_squared_error: 1.1225, scimark_benchmark: 949.4999,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7067, f_measure: 0.7729, kappa: 0.3028, kb_relative_information_score: 21.9019, mean_absolute_error: 0.2474, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7716, predictive_accuracy: 0.7742, prior_entropy: 0.7418, recall: 0.7742, relative_absolute_error: 0.7499, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4367, root_relative_squared_error: 1.0789, scimark_benchmark: 947.8244,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.796, f_measure: 0.8489, kappa: 0.5309, kb_relative_information_score: 46.3558, mean_absolute_error: 0.2061, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8469, predictive_accuracy: 0.8516, prior_entropy: 0.7418, recall: 0.8516, relative_absolute_error: 0.6248, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3528, root_relative_squared_error: 0.8715, scimark_benchmark: 919.0133,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6919, f_measure: 0.7911, kappa: 0.355, kb_relative_information_score: 22.6357, mean_absolute_error: 0.2653, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7889, predictive_accuracy: 0.7935, prior_entropy: 0.7418, recall: 0.7935, relative_absolute_error: 0.8042, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3887, root_relative_squared_error: 0.9603, scimark_benchmark: 947.7685,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5602, f_measure: 0.7938, kappa: 0.328, kb_relative_information_score: 23.7171, mean_absolute_error: 0.2745, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7905, predictive_accuracy: 0.8129, prior_entropy: 0.7418, recall: 0.8129, relative_absolute_error: 0.8321, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3949, root_relative_squared_error: 0.9756, scimark_benchmark: 924.3256,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7429, f_measure: 0.7569, kappa: 0.2453, kb_relative_information_score: 30.4493, mean_absolute_error: 0.2267, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.753, predictive_accuracy: 0.7613, prior_entropy: 0.7418, recall: 0.7613, relative_absolute_error: 0.6872, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.441, root_relative_squared_error: 1.0895, scimark_benchmark: 935.1217,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8153, f_measure: 0.7987, kappa: 0.3653, kb_relative_information_score: 48.0793, mean_absolute_error: 0.1965, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.794, predictive_accuracy: 0.8065, prior_entropy: 0.7418, recall: 0.8065, relative_absolute_error: 0.5957, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3806, root_relative_squared_error: 0.9402, scimark_benchmark: 948.4197,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.688, f_measure: 0.7719, kappa: 0.2806, kb_relative_information_score: 25.3066, mean_absolute_error: 0.2458, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7658, predictive_accuracy: 0.7806, prior_entropy: 0.7418, recall: 0.7806, relative_absolute_error: 0.7452, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3957, root_relative_squared_error: 0.9777, scimark_benchmark: 949.0349,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.798, f_measure: 0.7883, kappa: 0.3394, kb_relative_information_score: 26.2914, mean_absolute_error: 0.2379, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7843, predictive_accuracy: 0.7935, prior_entropy: 0.7418, recall: 0.7935, relative_absolute_error: 0.7211, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.396, root_relative_squared_error: 0.9784, scimark_benchmark: 935.6448,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8493, f_measure: 0.8502, kappa: 0.5227, kb_relative_information_score: 59.6222, mean_absolute_error: 0.1866, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8489, predictive_accuracy: 0.8581, prior_entropy: 0.7418, recall: 0.8581, relative_absolute_error: 0.5657, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3362, root_relative_squared_error: 0.8306, scimark_benchmark: 947.8115,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6952, f_measure: 0.7556, kappa: 0.2105, kb_relative_information_score: 19.1939, mean_absolute_error: 0.2675, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7458, predictive_accuracy: 0.7742, prior_entropy: 0.7418, recall: 0.7742, relative_absolute_error: 0.8109, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3997, root_relative_squared_error: 0.9875, scimark_benchmark: 920.6625,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4959, f_measure: 0.699, kappa: -0.0127, kb_relative_information_score: 38.2666, mean_absolute_error: 0.2129, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6287, predictive_accuracy: 0.7871, prior_entropy: 0.7418, recall: 0.7871, relative_absolute_error: 0.6454, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4614, root_relative_squared_error: 1.1399, scimark_benchmark: 916.8022,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7915, f_measure: 0.814, kappa: 0.4355, kb_relative_information_score: 52.8609, mean_absolute_error: 0.1846, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8151, predictive_accuracy: 0.8129, prior_entropy: 0.7418, recall: 0.8129, relative_absolute_error: 0.5596, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3912, root_relative_squared_error: 0.9666, scimark_benchmark: 946.8624,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.7022, kb_relative_information_score: 41.7587, mean_absolute_error: 0.2065, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6297, predictive_accuracy: 0.7935, prior_entropy: 0.7418, recall: 0.7935, relative_absolute_error: 0.6259, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4544, root_relative_squared_error: 1.1225, scimark_benchmark: 924.9487,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6416, f_measure: 0.7646, kappa: 0.243, kb_relative_information_score: 19.1504, mean_absolute_error: 0.272, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7561, predictive_accuracy: 0.7806, prior_entropy: 0.7418, recall: 0.7806, relative_absolute_error: 0.8246, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4056, root_relative_squared_error: 1.002, scimark_benchmark: 948.5995,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8397, f_measure: 0.7851, kappa: 0.2893, kb_relative_information_score: 28.8211, mean_absolute_error: 0.2544, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7871, predictive_accuracy: 0.8129, prior_entropy: 0.7418, recall: 0.8129, relative_absolute_error: 0.7711, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3495, root_relative_squared_error: 0.8634, scimark_benchmark: 925.6578,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8243, f_measure: 0.8175, kappa: 0.4216, kb_relative_information_score: 48.6713, mean_absolute_error: 0.2069, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8136, predictive_accuracy: 0.8258, prior_entropy: 0.7418, recall: 0.8258, relative_absolute_error: 0.6272, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3452, root_relative_squared_error: 0.8529, scimark_benchmark: 947.312,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.7022, kb_relative_information_score: 41.7587, mean_absolute_error: 0.2065, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6297, predictive_accuracy: 0.7935, prior_entropy: 0.7418, recall: 0.7935, relative_absolute_error: 0.6259, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4544, root_relative_squared_error: 1.1225, scimark_benchmark: 924.0403,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6012, f_measure: 0.82, kappa: 0.4211, kb_relative_information_score: 29.1674, mean_absolute_error: 0.269, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8175, predictive_accuracy: 0.8323, prior_entropy: 0.7418, recall: 0.8323, relative_absolute_error: 0.8154, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3755, root_relative_squared_error: 0.9277, scimark_benchmark: 988.1258,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.603, f_measure: 0.7938, kappa: 0.328, kb_relative_information_score: 26.5576, mean_absolute_error: 0.2725, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7905, predictive_accuracy: 0.8129, prior_entropy: 0.7418, recall: 0.8129, relative_absolute_error: 0.826, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3852, root_relative_squared_error: 0.9515, scimark_benchmark: 949.2926,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8265, f_measure: 0.7697, kappa: 0.2317, kb_relative_information_score: 25.33, mean_absolute_error: 0.2598, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7752, predictive_accuracy: 0.8065, prior_entropy: 0.7418, recall: 0.8065, relative_absolute_error: 0.7876, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3556, root_relative_squared_error: 0.8784, scimark_benchmark: 910.8768,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.7022, kb_relative_information_score: 41.7587, mean_absolute_error: 0.2065, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6297, predictive_accuracy: 0.7935, prior_entropy: 0.7418, recall: 0.7935, relative_absolute_error: 0.6259, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4544, root_relative_squared_error: 1.1225, scimark_benchmark: 947.3956,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8397, f_measure: 0.8421, kappa: 0.4945, kb_relative_information_score: 55.3531, mean_absolute_error: 0.194, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.841, predictive_accuracy: 0.8516, prior_entropy: 0.7418, recall: 0.8516, relative_absolute_error: 0.5882, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3433, root_relative_squared_error: 0.848, scimark_benchmark: 946.0642,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.7022, kb_relative_information_score: 41.7587, mean_absolute_error: 0.2065, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6297, predictive_accuracy: 0.7935, prior_entropy: 0.7418, recall: 0.7935, relative_absolute_error: 0.6259, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4544, root_relative_squared_error: 1.1225, scimark_benchmark: 947.9549,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7019, f_measure: 0.8284, kappa: 0.4506, kb_relative_information_score: 66.2038, mean_absolute_error: 0.1613, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.826, predictive_accuracy: 0.8387, prior_entropy: 0.7418, recall: 0.8387, relative_absolute_error: 0.489, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4016, root_relative_squared_error: 0.9922, scimark_benchmark: 939.8397,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4959, f_measure: 0.699, kappa: -0.0127, kb_relative_information_score: 38.2666, mean_absolute_error: 0.2129, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6287, predictive_accuracy: 0.7871, prior_entropy: 0.7418, recall: 0.7871, relative_absolute_error: 0.6454, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4614, root_relative_squared_error: 1.1399, scimark_benchmark: 922.1698,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4804, f_measure: 0.7045, kappa: 0.0076, kb_relative_information_score: -3.6635, mean_absolute_error: 0.3242, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6764, predictive_accuracy: 0.7613, prior_entropy: 0.7418, recall: 0.7613, relative_absolute_error: 0.9829, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4198, root_relative_squared_error: 1.037, scimark_benchmark: 913.234,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7019, f_measure: 0.8284, kappa: 0.4506, kb_relative_information_score: 66.2038, mean_absolute_error: 0.1613, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.826, predictive_accuracy: 0.8387, prior_entropy: 0.7418, recall: 0.8387, relative_absolute_error: 0.489, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4016, root_relative_squared_error: 0.9922, scimark_benchmark: 911.5134,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7449, f_measure: 0.8254, kappa: 0.436, kb_relative_information_score: 48.1609, mean_absolute_error: 0.2035, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8246, predictive_accuracy: 0.8387, prior_entropy: 0.7418, recall: 0.8387, relative_absolute_error: 0.6169, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3735, root_relative_squared_error: 0.9226, scimark_benchmark: 947.1623,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8415, f_measure: 0.7851, kappa: 0.2893, kb_relative_information_score: 28.8807, mean_absolute_error: 0.2537, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7871, predictive_accuracy: 0.8129, prior_entropy: 0.7418, recall: 0.8129, relative_absolute_error: 0.769, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3494, root_relative_squared_error: 0.8632, scimark_benchmark: 946.7262,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6874, f_measure: 0.8122, kappa: 0.4076, kb_relative_information_score: 54.3621, mean_absolute_error: 0.1852, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8081, predictive_accuracy: 0.8194, prior_entropy: 0.7418, recall: 0.8194, relative_absolute_error: 0.5613, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4221, root_relative_squared_error: 1.0427, scimark_benchmark: 928.3094,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8565, f_measure: 0.8558, kappa: 0.5385, kb_relative_information_score: 63.1794, mean_absolute_error: 0.18, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.856, predictive_accuracy: 0.8645, prior_entropy: 0.7418, recall: 0.8645, relative_absolute_error: 0.5456, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3266, root_relative_squared_error: 0.8069, scimark_benchmark: 944.9402,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8312, f_measure: 0.8302, kappa: 0.4759, kb_relative_information_score: 48.5603, mean_absolute_error: 0.2056, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8285, predictive_accuracy: 0.8323, prior_entropy: 0.7418, recall: 0.8323, relative_absolute_error: 0.6234, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3665, root_relative_squared_error: 0.9053, scimark_benchmark: 946.0982,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.7022, kb_relative_information_score: 41.7587, mean_absolute_error: 0.2065, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6297, predictive_accuracy: 0.7935, prior_entropy: 0.7418, recall: 0.7935, relative_absolute_error: 0.6259, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4544, root_relative_squared_error: 1.1225, scimark_benchmark: 944.9313,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6068, f_measure: 0.8309, kappa: 0.4513, kb_relative_information_score: 35.0558, mean_absolute_error: 0.2593, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.8323, predictive_accuracy: 0.8452, prior_entropy: 0.7418, recall: 0.8452, relative_absolute_error: 0.786, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3611, root_relative_squared_error: 0.8922, scimark_benchmark: 912.5367,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7312, f_measure: 0.7653, kappa: 0.2353, kb_relative_information_score: 10.8568, mean_absolute_error: 0.2662, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.7571, predictive_accuracy: 0.7871, prior_entropy: 0.7418, recall: 0.7871, relative_absolute_error: 0.8069, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.3979, root_relative_squared_error: 0.983, scimark_benchmark: 946.3967,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.7022, kb_relative_information_score: 41.7587, mean_absolute_error: 0.2065, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.6297, predictive_accuracy: 0.7935, prior_entropy: 0.7418, recall: 0.7935, relative_absolute_error: 0.6259, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4544, root_relative_squared_error: 1.1225, scimark_benchmark: 921.9694,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.67, f_measure: 0.7987, kappa: 0.3653, kb_relative_information_score: 48.7431, mean_absolute_error: 0.1935, mean_prior_absolute_error: 0.3299, number_of_instances: 155, precision: 0.794, predictive_accuracy: 0.8065, prior_entropy: 0.7418, recall: 0.8065, relative_absolute_error: 0.5868, root_mean_prior_squared_error: 0.4048, root_mean_squared_error: 0.4399, root_relative_squared_error: 1.0869, scimark_benchmark: 883.4587,

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