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OpenML
Task
Supervised Classification on chscase_vine1

Supervised Classification on chscase_vine1

Task 3680 Supervised Classification chscase_vine1 509 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 under100k under1m
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Metric:

509 Runs

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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8839, f_measure: 0.8068, kappa: 0.6108, kb_relative_information_score: 28.0051, mean_absolute_error: 0.2346, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8084, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.4718, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.369, root_relative_squared_error: 0.7402, scimark_benchmark: 854.5188,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8006, f_measure: 0.8054, kappa: 0.6084, kb_relative_information_score: 31.8248, mean_absolute_error: 0.1923, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.813, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.3868, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4385, root_relative_squared_error: 0.8797, scimark_benchmark: 889.3151, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.3769, kb_relative_information_score: 3.6019, mean_absolute_error: 0.4615, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.2899, predictive_accuracy: 0.5385, prior_entropy: 0.996, recall: 0.5385, relative_absolute_error: 0.9284, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.6794, root_relative_squared_error: 1.3628, scimark_benchmark: 1333.5799,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8452, f_measure: 0.8462, kappa: 0.6905, kb_relative_information_score: 35.8567, mean_absolute_error: 0.1538, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8462, predictive_accuracy: 0.8462, prior_entropy: 0.996, recall: 0.8462, relative_absolute_error: 0.3095, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3922, root_relative_squared_error: 0.7868, scimark_benchmark: 1297.6599, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8527, f_measure: 0.7868, kappa: 0.5706, kb_relative_information_score: 21.4261, mean_absolute_error: 0.3092, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7905, predictive_accuracy: 0.7885, prior_entropy: 0.996, recall: 0.7885, relative_absolute_error: 0.6219, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3902, root_relative_squared_error: 0.7826, scimark_benchmark: 938.343, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7649, f_measure: 0.7682, kappa: 0.5329, kb_relative_information_score: 27.793, mean_absolute_error: 0.2308, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7695, predictive_accuracy: 0.7692, prior_entropy: 0.996, recall: 0.7692, relative_absolute_error: 0.4642, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4804, root_relative_squared_error: 0.9636, scimark_benchmark: 938.343, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8958, f_measure: 0.8266, kappa: 0.6507, kb_relative_information_score: 27.6516, mean_absolute_error: 0.2473, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8269, predictive_accuracy: 0.8269, prior_entropy: 0.996, recall: 0.8269, relative_absolute_error: 0.4975, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3471, root_relative_squared_error: 0.6963, scimark_benchmark: 934.4732, usercpu_time_millis: 40, usercpu_time_millis_training: 40,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8512, f_measure: 0.8271, kappa: 0.6528, kb_relative_information_score: 30.804, mean_absolute_error: 0.2092, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8279, predictive_accuracy: 0.8269, prior_entropy: 0.996, recall: 0.8269, relative_absolute_error: 0.4208, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3785, root_relative_squared_error: 0.7592, scimark_benchmark: 1250.8301, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9368, f_measure: 0.8077, kappa: 0.6131, kb_relative_information_score: 29.773, mean_absolute_error: 0.2235, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8077, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.4495, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3263, root_relative_squared_error: 0.6545, scimark_benchmark: 1318.1432, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7917, f_measure: 0.7887, kappa: 0.5782, kb_relative_information_score: 29.8089, mean_absolute_error: 0.2115, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7942, predictive_accuracy: 0.7885, prior_entropy: 0.996, recall: 0.7885, relative_absolute_error: 0.4255, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4599, root_relative_squared_error: 0.9226, scimark_benchmark: 1319.9043,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7917, f_measure: 0.8271, kappa: 0.6528, kb_relative_information_score: 26.3773, mean_absolute_error: 0.2626, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8279, predictive_accuracy: 0.8269, prior_entropy: 0.996, recall: 0.8269, relative_absolute_error: 0.5283, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3918, root_relative_squared_error: 0.7859, scimark_benchmark: 1315.3881,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7701, f_measure: 0.7692, kappa: 0.5412, kb_relative_information_score: 23.3309, mean_absolute_error: 0.2843, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7784, predictive_accuracy: 0.7692, prior_entropy: 0.996, recall: 0.7692, relative_absolute_error: 0.5719, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4316, root_relative_squared_error: 0.8658, scimark_benchmark: 1336.3256,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8482, f_measure: 0.8464, kappa: 0.6923, kb_relative_information_score: 35.8567, mean_absolute_error: 0.1538, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8491, predictive_accuracy: 0.8462, prior_entropy: 0.996, recall: 0.8462, relative_absolute_error: 0.3095, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3922, root_relative_squared_error: 0.7868, scimark_benchmark: 1291.6995,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5863, f_measure: 0.4923, kappa: 0.1625, kb_relative_information_score: 5.6178, mean_absolute_error: 0.4423, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.6974, predictive_accuracy: 0.5577, prior_entropy: 0.996, recall: 0.5577, relative_absolute_error: 0.8897, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.6651, root_relative_squared_error: 1.3341, scimark_benchmark: 1280.6952,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8289, f_measure: 0.8077, kappa: 0.6131, kb_relative_information_score: 30.5048, mean_absolute_error: 0.208, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8077, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.4184, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4265, root_relative_squared_error: 0.8556, scimark_benchmark: 1465.2979,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8274, f_measure: 0.8271, kappa: 0.6528, kb_relative_information_score: 33.8408, mean_absolute_error: 0.1731, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8279, predictive_accuracy: 0.8269, prior_entropy: 0.996, recall: 0.8269, relative_absolute_error: 0.3481, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.416, root_relative_squared_error: 0.8345, scimark_benchmark: 1465.2979,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8512, f_measure: 0.8462, kappa: 0.6941, kb_relative_information_score: 35.8567, mean_absolute_error: 0.1538, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8562, predictive_accuracy: 0.8462, prior_entropy: 0.996, recall: 0.8462, relative_absolute_error: 0.3095, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3922, root_relative_squared_error: 0.7868, scimark_benchmark: 1353.5686,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4196, f_measure: 0.3769, kb_relative_information_score: -0.1942, mean_absolute_error: 0.4984, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.2899, predictive_accuracy: 0.5385, prior_entropy: 0.996, recall: 0.5385, relative_absolute_error: 1.0026, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4999, root_relative_squared_error: 1.0027, scimark_benchmark: 1353.5686,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.811, f_measure: 0.8068, kappa: 0.6108, kb_relative_information_score: 30.8573, mean_absolute_error: 0.2047, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8084, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.4118, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4311, root_relative_squared_error: 0.8647, scimark_benchmark: 1466.6185,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8705, f_measure: 0.8077, kappa: 0.6131, kb_relative_information_score: 30.7486, mean_absolute_error: 0.2033, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8077, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.409, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4372, root_relative_squared_error: 0.8769, scimark_benchmark: 931.2336, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8728, f_measure: 0.8462, kappa: 0.6905, kb_relative_information_score: 34.3987, mean_absolute_error: 0.1706, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8462, predictive_accuracy: 0.8462, prior_entropy: 0.996, recall: 0.8462, relative_absolute_error: 0.3432, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3921, root_relative_squared_error: 0.7866, scimark_benchmark: 945.6434, usercpu_time_millis: 20, usercpu_time_millis_testing: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8698, f_measure: 0.8266, kappa: 0.6507, kb_relative_information_score: 30.7641, mean_absolute_error: 0.2058, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8269, predictive_accuracy: 0.8269, prior_entropy: 0.996, recall: 0.8269, relative_absolute_error: 0.4139, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4053, root_relative_squared_error: 0.8131, scimark_benchmark: 941.7954, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8891, f_measure: 0.8077, kappa: 0.6131, kb_relative_information_score: 29.0828, mean_absolute_error: 0.222, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8077, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.4465, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3997, root_relative_squared_error: 0.8019, scimark_benchmark: 923.7642, usercpu_time_millis: 20, usercpu_time_millis_testing: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8891, f_measure: 0.8077, kappa: 0.6131, kb_relative_information_score: 29.0828, mean_absolute_error: 0.222, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8077, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.4465, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3997, root_relative_squared_error: 0.8019, scimark_benchmark: 894.7455, usercpu_time_millis: 20, usercpu_time_millis_testing: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8795, f_measure: 0.7881, kappa: 0.5731, kb_relative_information_score: 29.5734, mean_absolute_error: 0.2143, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7883, predictive_accuracy: 0.7885, prior_entropy: 0.996, recall: 0.7885, relative_absolute_error: 0.4311, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4585, root_relative_squared_error: 0.9197, scimark_benchmark: 936.7115, usercpu_time_millis: 40, usercpu_time_millis_training: 40,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8899, f_measure: 0.8077, kappa: 0.6131, kb_relative_information_score: 31.7796, mean_absolute_error: 0.1928, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8077, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.3878, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4328, root_relative_squared_error: 0.8682, scimark_benchmark: 938.2848, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8839, f_measure: 0.8077, kappa: 0.6131, kb_relative_information_score: 32.4422, mean_absolute_error: 0.1858, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8077, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.3737, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4133, root_relative_squared_error: 0.8292, scimark_benchmark: 942.1229,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8757, f_measure: 0.7881, kappa: 0.5731, kb_relative_information_score: 30.7386, mean_absolute_error: 0.2002, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7883, predictive_accuracy: 0.7885, prior_entropy: 0.996, recall: 0.7885, relative_absolute_error: 0.4026, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4229, root_relative_squared_error: 0.8483, scimark_benchmark: 933.8635,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8854, f_measure: 0.808, kappa: 0.6154, kb_relative_information_score: 32.5948, mean_absolute_error: 0.1842, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8107, predictive_accuracy: 0.8077, prior_entropy: 0.996, recall: 0.8077, relative_absolute_error: 0.3705, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4099, root_relative_squared_error: 0.8222, scimark_benchmark: 938.4285,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8899, f_measure: 0.7887, kappa: 0.5757, kb_relative_information_score: 30.6652, mean_absolute_error: 0.2031, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7895, predictive_accuracy: 0.7885, prior_entropy: 0.996, recall: 0.7885, relative_absolute_error: 0.4086, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.4389, root_relative_squared_error: 0.8804, scimark_benchmark: 936.6206, usercpu_time_millis: 30, usercpu_time_millis_training: 30,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8824, f_measure: 0.7887, kappa: 0.5757, kb_relative_information_score: 29.0562, mean_absolute_error: 0.2173, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7895, predictive_accuracy: 0.7885, prior_entropy: 0.996, recall: 0.7885, relative_absolute_error: 0.4371, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.444, root_relative_squared_error: 0.8907, scimark_benchmark: 924.6116, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8973, f_measure: 0.7887, kappa: 0.5757, kb_relative_information_score: 30.3459, mean_absolute_error: 0.2032, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7895, predictive_accuracy: 0.7885, prior_entropy: 0.996, recall: 0.7885, relative_absolute_error: 0.4087, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.411, root_relative_squared_error: 0.8245, scimark_benchmark: 924.6116, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9048, f_measure: 0.8266, kappa: 0.6507, kb_relative_information_score: 31.1802, mean_absolute_error: 0.199, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8269, predictive_accuracy: 0.8269, prior_entropy: 0.996, recall: 0.8269, relative_absolute_error: 0.4002, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3924, root_relative_squared_error: 0.7872, scimark_benchmark: 947.9494, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9048, f_measure: 0.7881, kappa: 0.5731, kb_relative_information_score: 29.1386, mean_absolute_error: 0.2218, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.7883, predictive_accuracy: 0.7885, prior_entropy: 0.996, recall: 0.7885, relative_absolute_error: 0.4462, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3824, root_relative_squared_error: 0.767, scimark_benchmark: 929.566,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9524, build_cpu_time: 0.0319, build_memory: 214414051.0769, f_measure: 0.8846, kappa: 0.7679, kb_relative_information_score: 40.3922, mean_absolute_error: 0.1108, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8846, predictive_accuracy: 0.8846, prior_entropy: 0.996, recall: 0.8846, relative_absolute_error: 0.2228, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3069, root_relative_squared_error: 0.6156, scimark_benchmark: 939.6298,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.875, build_cpu_time: 0.0243, build_memory: 831256602.1538, f_measure: 0.8651, kappa: 0.7284, kb_relative_information_score: 37.4708, mean_absolute_error: 0.14, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8655, predictive_accuracy: 0.8654, prior_entropy: 0.996, recall: 0.8654, relative_absolute_error: 0.2817, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3541, root_relative_squared_error: 0.7104, scimark_benchmark: 933.4498,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8705, build_cpu_time: 0.0122, build_memory: 1782813973.8462, f_measure: 0.8651, kappa: 0.7284, kb_relative_information_score: 36.8646, mean_absolute_error: 0.1465, mean_prior_absolute_error: 0.4972, number_of_instances: 52, precision: 0.8655, predictive_accuracy: 0.8654, prior_entropy: 0.996, recall: 0.8654, relative_absolute_error: 0.2947, root_mean_prior_squared_error: 0.4985, root_mean_squared_error: 0.3575, root_relative_squared_error: 0.717, scimark_benchmark: 916.8516,

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