OpenML
Supervised Classification on iris

Supervised Classification on iris

Task 7306 Supervised Classification iris 478 runs submitted
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  • study_25 study_7 under100k under1m
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9638, average_cost: 0.8333, f_measure: 0.9466, kappa: 0.92, kb_relative_information_score: 137.4407, mean_absolute_error: 0.042, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9471, predictive_accuracy: 0.9467, prior_entropy: 1.585, recall: 0.9467, relative_absolute_error: 0.0946, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1896, root_relative_squared_error: 0.4022, scimark_benchmark: 939.9176, total_cost: 125,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.99, average_cost: 0.8667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 130.6035, mean_absolute_error: 0.0774, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.1742, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1571, root_relative_squared_error: 0.3333, scimark_benchmark: 1324.5988, total_cost: 130, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9669, average_cost: 1.1667, f_measure: 0.9333, kappa: 0.9, kb_relative_information_score: 135.7807, mean_absolute_error: 0.0462, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9333, predictive_accuracy: 0.9333, prior_entropy: 1.585, recall: 0.9333, relative_absolute_error: 0.1041, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.2071, root_relative_squared_error: 0.4393, scimark_benchmark: 1337.5749, total_cost: 175, usercpu_time_millis: 30, usercpu_time_millis_training: 30,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9779, average_cost: 0.9333, f_measure: 0.9467, kappa: 0.92, kb_relative_information_score: 138.1914, mean_absolute_error: 0.0393, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9467, predictive_accuracy: 0.9467, prior_entropy: 1.585, recall: 0.9467, relative_absolute_error: 0.0885, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1868, root_relative_squared_error: 0.3962, scimark_benchmark: 1348.7888, total_cost: 140, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9957, average_cost: 0.7667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 136.8538, mean_absolute_error: 0.0485, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.1092, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.147, root_relative_squared_error: 0.3118, scimark_benchmark: 939.5088, total_cost: 115, usercpu_time_millis: 30, usercpu_time_millis_training: 30,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9819, average_cost: 1.1667, f_measure: 0.9333, kappa: 0.9, kb_relative_information_score: 138.0315, mean_absolute_error: 0.0415, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9333, predictive_accuracy: 0.9333, prior_entropy: 1.585, recall: 0.9333, relative_absolute_error: 0.0935, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1596, root_relative_squared_error: 0.3386, scimark_benchmark: 854.5188, total_cost: 175,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9947, average_cost: 0.6667, f_measure: 0.96, kappa: 0.94, kb_relative_information_score: 119.0466, mean_absolute_error: 0.1238, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.96, predictive_accuracy: 0.96, prior_entropy: 1.585, recall: 0.96, relative_absolute_error: 0.2785, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1851, root_relative_squared_error: 0.3928, scimark_benchmark: 932.0242, total_cost: 100, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9789, average_cost: 0.4667, f_measure: 0.9733, kappa: 0.96, kb_relative_information_score: 143.0152, mean_absolute_error: 0.024, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9733, predictive_accuracy: 0.9733, prior_entropy: 1.585, recall: 0.9733, relative_absolute_error: 0.0539, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.133, root_relative_squared_error: 0.2821, scimark_benchmark: 1345.7204, total_cost: 70,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9679, average_cost: 1.2667, f_measure: 0.9333, kappa: 0.9, kb_relative_information_score: 122.1549, mean_absolute_error: 0.1036, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9338, predictive_accuracy: 0.9333, prior_entropy: 1.585, recall: 0.9333, relative_absolute_error: 0.233, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.2275, root_relative_squared_error: 0.4827, scimark_benchmark: 1347.9553, total_cost: 190, usercpu_time_millis: 30, usercpu_time_millis_testing: 30,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9779, average_cost: 0.7667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 139.2077, mean_absolute_error: 0.0369, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.083, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1726, root_relative_squared_error: 0.366, scimark_benchmark: 1308.3432, total_cost: 115,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9961, average_cost: 0.9333, f_measure: 0.9467, kappa: 0.92, kb_relative_information_score: 137.8837, mean_absolute_error: 0.0416, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9467, predictive_accuracy: 0.9467, prior_entropy: 1.585, recall: 0.9467, relative_absolute_error: 0.0936, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.149, root_relative_squared_error: 0.3161, scimark_benchmark: 1337.8643, total_cost: 140, usercpu_time_millis: 50, usercpu_time_millis_testing: 50,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9665, average_cost: 0.8667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 138.6044, mean_absolute_error: 0.0399, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.0898, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1747, root_relative_squared_error: 0.3707, scimark_benchmark: 898.1296, total_cost: 130,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.965, average_cost: 0.8667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 140.4165, mean_absolute_error: 0.0311, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.07, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1764, root_relative_squared_error: 0.3742, scimark_benchmark: 1298.4923, total_cost: 130,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9919, average_cost: 0.6333, f_measure: 0.9667, kappa: 0.95, kb_relative_information_score: 121.6033, mean_absolute_error: 0.1101, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9668, predictive_accuracy: 0.9667, prior_entropy: 1.585, recall: 0.9667, relative_absolute_error: 0.2477, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1878, root_relative_squared_error: 0.3983, scimark_benchmark: 1345.4669, total_cost: 95,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, average_cost: 10, f_measure: 0.1667, kb_relative_information_score: -0.0001, mean_absolute_error: 0.4444, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.1111, predictive_accuracy: 0.3333, prior_entropy: 1.585, recall: 0.3333, relative_absolute_error: 1, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.4714, root_relative_squared_error: 1, scimark_benchmark: 937.5996, total_cost: 1500,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.965, average_cost: 0.5667, f_measure: 0.9536, kappa: 0.93, kb_relative_information_score: 140.4165, mean_absolute_error: 0.0311, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9564, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.07, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1764, root_relative_squared_error: 0.3742, scimark_benchmark: 909.1698, total_cost: 85, usercpu_time_millis: 40, usercpu_time_millis_training: 40,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8333, average_cost: 3.3333, f_measure: 0.5556, kappa: 0.5, kb_relative_information_score: 86.907, mean_absolute_error: 0.2222, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.5, predictive_accuracy: 0.6667, prior_entropy: 1.585, recall: 0.6667, relative_absolute_error: 0.5, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.3333, root_relative_squared_error: 0.7071, scimark_benchmark: 765.566, total_cost: 500,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9632, average_cost: 1.0667, f_measure: 0.9333, kappa: 0.9, kb_relative_information_score: 125.9632, mean_absolute_error: 0.0918, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9338, predictive_accuracy: 0.9333, prior_entropy: 1.585, recall: 0.9333, relative_absolute_error: 0.2065, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.2025, root_relative_squared_error: 0.4296, scimark_benchmark: 765.566, total_cost: 160,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9707, average_cost: 0.9667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 137.5543, mean_absolute_error: 0.0441, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9544, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.0992, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1759, root_relative_squared_error: 0.3732, scimark_benchmark: 1316.7714, total_cost: 145,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.97, average_cost: 0.8, f_measure: 0.96, kappa: 0.94, kb_relative_information_score: 141.7856, mean_absolute_error: 0.0267, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.961, predictive_accuracy: 0.96, prior_entropy: 1.585, recall: 0.96, relative_absolute_error: 0.06, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1633, root_relative_squared_error: 0.3464, scimark_benchmark: 1316.8289, total_cost: 120,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9896, average_cost: 0.8, f_measure: 0.96, kappa: 0.94, kb_relative_information_score: 129.909, mean_absolute_error: 0.0801, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9605, predictive_accuracy: 0.96, prior_entropy: 1.585, recall: 0.96, relative_absolute_error: 0.1802, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1605, root_relative_squared_error: 0.3405, scimark_benchmark: 1292.1375, total_cost: 120, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9781, average_cost: 0.9333, f_measure: 0.9467, kappa: 0.92, kb_relative_information_score: 138.1874, mean_absolute_error: 0.0392, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9467, predictive_accuracy: 0.9467, prior_entropy: 1.585, recall: 0.9467, relative_absolute_error: 0.0881, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1777, root_relative_squared_error: 0.377, scimark_benchmark: 1316.8289, total_cost: 140,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9928, average_cost: 0.7667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 137.2053, mean_absolute_error: 0.0469, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.1055, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1491, root_relative_squared_error: 0.3163, scimark_benchmark: 1311.1837, total_cost: 115,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9935, average_cost: 0.7667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 136.5994, mean_absolute_error: 0.0479, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.1079, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1575, root_relative_squared_error: 0.3341, scimark_benchmark: 1182.3687, total_cost: 115,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.985, average_cost: 0.5, f_measure: 0.98, kappa: 0.97, kb_relative_information_score: 145.8928, mean_absolute_error: 0.0133, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9811, predictive_accuracy: 0.98, prior_entropy: 1.585, recall: 0.98, relative_absolute_error: 0.03, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1155, root_relative_squared_error: 0.2449, scimark_benchmark: 1345.842, total_cost: 75, usercpu_time_millis: 10, usercpu_time_millis_testing: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9555, average_cost: 2.0667, f_measure: 0.8935, kappa: 0.84, kb_relative_information_score: 125.4187, mean_absolute_error: 0.0834, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.8959, predictive_accuracy: 0.8933, prior_entropy: 1.585, recall: 0.8933, relative_absolute_error: 0.1877, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.2419, root_relative_squared_error: 0.5132, scimark_benchmark: 854.6529, total_cost: 310,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9555, average_cost: 2.0667, f_measure: 0.8935, kappa: 0.84, kb_relative_information_score: 125.4187, mean_absolute_error: 0.0834, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.8959, predictive_accuracy: 0.8933, prior_entropy: 1.585, recall: 0.8933, relative_absolute_error: 0.1877, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.2419, root_relative_squared_error: 0.5132, scimark_benchmark: 1316.535, total_cost: 310, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9648, average_cost: 1.1, f_measure: 0.94, kappa: 0.91, kb_relative_information_score: 133.5813, mean_absolute_error: 0.0591, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9401, predictive_accuracy: 0.94, prior_entropy: 1.585, recall: 0.94, relative_absolute_error: 0.133, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1933, root_relative_squared_error: 0.4101, scimark_benchmark: 1330.7934, total_cost: 165,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9928, average_cost: 0.8667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 138.1348, mean_absolute_error: 0.0408, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.0918, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1554, root_relative_squared_error: 0.3295, scimark_benchmark: 1350.9691, total_cost: 130,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9951, average_cost: 0.7667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 140.1339, mean_absolute_error: 0.0338, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.0761, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1539, root_relative_squared_error: 0.3265, scimark_benchmark: 1358.4523, total_cost: 115,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9774, average_cost: 1.3667, f_measure: 0.9133, kappa: 0.87, kb_relative_information_score: 65.1621, mean_absolute_error: 0.3034, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9142, predictive_accuracy: 0.9133, prior_entropy: 1.585, recall: 0.9133, relative_absolute_error: 0.6827, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.3372, root_relative_squared_error: 0.7153, scimark_benchmark: 1342.5243, total_cost: 205,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9834, average_cost: 0.8667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 136.5743, mean_absolute_error: 0.0481, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.1082, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1723, root_relative_squared_error: 0.3654, scimark_benchmark: 1348.8402, total_cost: 130, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9779, average_cost: 0.7667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 139.2077, mean_absolute_error: 0.0369, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.083, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1726, root_relative_squared_error: 0.366, scimark_benchmark: 1340.0078, total_cost: 115,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9951, average_cost: 0.7667, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 136.5316, mean_absolute_error: 0.0496, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, recall: 0.9533, relative_absolute_error: 0.1116, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1494, root_relative_squared_error: 0.3169, scimark_benchmark: 1338.01, total_cost: 115, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9565, average_cost: 0.8333, f_measure: 0.9471, kappa: 0.92, kb_relative_information_score: 137.3715, mean_absolute_error: 0.0436, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9479, predictive_accuracy: 0.9467, prior_entropy: 1.585, recall: 0.9467, relative_absolute_error: 0.0982, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.183, root_relative_squared_error: 0.3881, scimark_benchmark: 1341.7207, total_cost: 125, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9818, average_cost: 0.9333, f_measure: 0.9467, kappa: 0.92, kb_relative_information_score: 139.4003, mean_absolute_error: 0.0345, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9467, predictive_accuracy: 0.9467, prior_entropy: 1.585, recall: 0.9467, relative_absolute_error: 0.0776, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1712, root_relative_squared_error: 0.3632, scimark_benchmark: 939.9176, total_cost: 140, usercpu_time_millis: 60, usercpu_time_millis_training: 60,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9863, average_cost: 0.6333, f_measure: 0.9667, kappa: 0.95, kb_relative_information_score: 139.8169, mean_absolute_error: 0.036, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9668, predictive_accuracy: 0.9667, prior_entropy: 1.585, recall: 0.9667, relative_absolute_error: 0.081, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1567, root_relative_squared_error: 0.3323, scimark_benchmark: 854.5188, total_cost: 95, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9724, average_cost: 0.8333, f_measure: 0.9466, kappa: 0.92, kb_relative_information_score: 133.4337, mean_absolute_error: 0.0612, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9471, predictive_accuracy: 0.9467, prior_entropy: 1.585, recall: 0.9467, relative_absolute_error: 0.1378, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1782, root_relative_squared_error: 0.3779, scimark_benchmark: 932.3943, total_cost: 125, usercpu_time_millis: 20, usercpu_time_millis_testing: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, average_cost: 10, f_measure: 0.1667, kb_relative_information_score: 13.093, mean_absolute_error: 0.4444, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.1111, predictive_accuracy: 0.3333, prior_entropy: 1.585, recall: 0.3333, relative_absolute_error: 1, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.6667, root_relative_squared_error: 1.4142, scimark_benchmark: 901.0726, total_cost: 1500,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.959, average_cost: 1.1667, f_measure: 0.9337, kappa: 0.9, kb_relative_information_score: 135.3386, mean_absolute_error: 0.0504, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9346, predictive_accuracy: 0.9333, prior_entropy: 1.585, recall: 0.9333, relative_absolute_error: 0.1134, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.192, root_relative_squared_error: 0.4072, scimark_benchmark: 902.4773, total_cost: 175, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9905, average_cost: 1, f_measure: 0.94, kappa: 0.91, kb_relative_information_score: 121.216, mean_absolute_error: 0.1103, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9401, predictive_accuracy: 0.94, prior_entropy: 1.585, recall: 0.94, relative_absolute_error: 0.2483, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.184, root_relative_squared_error: 0.3902, scimark_benchmark: 943.2745, total_cost: 150, usercpu_time_millis: 60, usercpu_time_millis_training: 60,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9867, average_cost: 1.1, f_measure: 0.94, kappa: 0.91, kb_relative_information_score: 81.6597, mean_absolute_error: 0.2525, mean_prior_absolute_error: 0.4444, number_of_instances: 150, precision: 0.9401, predictive_accuracy: 0.94, prior_entropy: 1.585, recall: 0.94, relative_absolute_error: 0.5681, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.3014, root_relative_squared_error: 0.6393, scimark_benchmark: 947.9494, total_cost: 165,

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