Task
Supervised Classification on breast-cancer

Supervised Classification on breast-cancer

Task 13 Supervised Classification breast-cancer 767 runs submitted
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  • basic mythbusting mythbusting_1 study_1 study_107 study_15 study_20 study_41 study_73 under100k under1m
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6832, f_measure: 0.6859, kappa: 0.2226, kb_relative_information_score: 33.6857, mean_absolute_error: 0.3581, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6804, predictive_accuracy: 0.7028, prior_entropy: 0.8796, recall: 0.7028, relative_absolute_error: 0.8561, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4402, root_relative_squared_error: 0.9631, scimark_benchmark: 1984.7669,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6782, f_measure: 0.6886, kappa: 0.2241, kb_relative_information_score: 34.374, mean_absolute_error: 0.3558, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6867, predictive_accuracy: 0.7133, prior_entropy: 0.8796, recall: 0.7133, relative_absolute_error: 0.8506, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4444, root_relative_squared_error: 0.9723, scimark_benchmark: 908.2939,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6829, f_measure: 0.6496, kappa: 0.1252, kb_relative_information_score: 28.358, mean_absolute_error: 0.3695, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6592, predictive_accuracy: 0.7028, prior_entropy: 0.8796, recall: 0.7028, relative_absolute_error: 0.8833, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4384, root_relative_squared_error: 0.9593, scimark_benchmark: 949.6132,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6498, f_measure: 0.6728, kappa: 0.1862, kb_relative_information_score: 31.6072, mean_absolute_error: 0.3614, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6672, predictive_accuracy: 0.6958, prior_entropy: 0.8796, recall: 0.6958, relative_absolute_error: 0.8639, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4512, root_relative_squared_error: 0.9873, scimark_benchmark: 945.9803,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5932, f_measure: 0.7128, kappa: 0.2826, kb_relative_information_score: 38.1427, mean_absolute_error: 0.3681, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7524, predictive_accuracy: 0.7552, prior_entropy: 0.8796, recall: 0.7552, relative_absolute_error: 0.8801, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4343, root_relative_squared_error: 0.9503, scimark_benchmark: 943.6022,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5898, f_measure: 0.6489, kappa: 0.1384, kb_relative_information_score: 9.7273, mean_absolute_error: 0.3876, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6414, predictive_accuracy: 0.6608, prior_entropy: 0.8796, recall: 0.6608, relative_absolute_error: 0.9267, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4933, root_relative_squared_error: 1.0794, scimark_benchmark: 938.5939,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5935, f_measure: 0.7128, kappa: 0.2826, kb_relative_information_score: 38.5871, mean_absolute_error: 0.3678, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7524, predictive_accuracy: 0.7552, prior_entropy: 0.8796, recall: 0.7552, relative_absolute_error: 0.8792, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4362, root_relative_squared_error: 0.9544, scimark_benchmark: 945.6864,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.5801, kb_relative_information_score: 67.4849, mean_absolute_error: 0.2972, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.4939, predictive_accuracy: 0.7028, prior_entropy: 0.8796, recall: 0.7028, relative_absolute_error: 0.7105, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5452, root_relative_squared_error: 1.1928, scimark_benchmark: 946.8378,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6608, f_measure: 0.6881, kappa: 0.2211, kb_relative_information_score: 48.1957, mean_absolute_error: 0.3317, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7154, predictive_accuracy: 0.7343, prior_entropy: 0.8796, recall: 0.7343, relative_absolute_error: 0.7929, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4551, root_relative_squared_error: 0.9958, scimark_benchmark: 885.9577,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5749, f_measure: 0.6653, kappa: 0.1675, kb_relative_information_score: 57.2283, mean_absolute_error: 0.3112, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6586, predictive_accuracy: 0.6888, prior_entropy: 0.8796, recall: 0.6888, relative_absolute_error: 0.7439, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5578, root_relative_squared_error: 1.2206, scimark_benchmark: 873.685,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5213, f_measure: 0.6164, kappa: 0.0558, kb_relative_information_score: 64.9207, mean_absolute_error: 0.3007, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6392, predictive_accuracy: 0.6993, prior_entropy: 0.8796, recall: 0.6993, relative_absolute_error: 0.7188, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5484, root_relative_squared_error: 1.1998,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5903, f_measure: 0.6824, kappa: 0.2409, kb_relative_information_score: 16.8905, mean_absolute_error: 0.3867, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6829, predictive_accuracy: 0.6818, prior_entropy: 0.8796, recall: 0.6818, relative_absolute_error: 0.9244, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4476, root_relative_squared_error: 0.9794, scimark_benchmark: 914.0905,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6527, f_measure: 0.6822, kappa: 0.2058, kb_relative_information_score: 17.933, mean_absolute_error: 0.3824, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6834, predictive_accuracy: 0.7133, prior_entropy: 0.8796, recall: 0.7133, relative_absolute_error: 0.9141, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4493, root_relative_squared_error: 0.9831, scimark_benchmark: 946.2095,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6578, f_measure: 0.7237, kappa: 0.3119, kb_relative_information_score: 50.8283, mean_absolute_error: 0.3336, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7265, predictive_accuracy: 0.7448, prior_entropy: 0.8796, recall: 0.7448, relative_absolute_error: 0.7975, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4507, root_relative_squared_error: 0.9862, scimark_benchmark: 945.5845,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5229, f_measure: 0.6159, kappa: 0.061, kb_relative_information_score: 70.049, mean_absolute_error: 0.2937, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.663, predictive_accuracy: 0.7063, prior_entropy: 0.8796, recall: 0.7063, relative_absolute_error: 0.7021, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5419, root_relative_squared_error: 1.1858, scimark_benchmark: 924.8462,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6533, f_measure: 0.7046, kappa: 0.2632, kb_relative_information_score: 44.3916, mean_absolute_error: 0.344, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7514, predictive_accuracy: 0.7517, prior_entropy: 0.8796, recall: 0.7517, relative_absolute_error: 0.8224, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.442, root_relative_squared_error: 0.9671, scimark_benchmark: 920.6594,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5276, f_measure: 0.6266, kappa: 0.0663, kb_relative_information_score: 46.9718, mean_absolute_error: 0.3252, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6188, predictive_accuracy: 0.6748, prior_entropy: 0.8796, recall: 0.6748, relative_absolute_error: 0.7773, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5702, root_relative_squared_error: 1.2477, scimark_benchmark: 875.7558,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5943, f_measure: 0.7128, kappa: 0.2826, kb_relative_information_score: 38.5654, mean_absolute_error: 0.367, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7524, predictive_accuracy: 0.7552, prior_entropy: 0.8796, recall: 0.7552, relative_absolute_error: 0.8774, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4317, root_relative_squared_error: 0.9446, scimark_benchmark: 949.0095,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5229, f_measure: 0.6159, kappa: 0.061, kb_relative_information_score: 70.049, mean_absolute_error: 0.2937, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.663, predictive_accuracy: 0.7063, prior_entropy: 0.8796, recall: 0.7063, relative_absolute_error: 0.7021, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5419, root_relative_squared_error: 1.1858, scimark_benchmark: 922.9472,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6678, f_measure: 0.6809, kappa: 0.2087, kb_relative_information_score: 42.1018, mean_absolute_error: 0.3533, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7462, predictive_accuracy: 0.7413, prior_entropy: 0.8796, recall: 0.7413, relative_absolute_error: 0.8446, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4356, root_relative_squared_error: 0.9531, scimark_benchmark: 917.8713,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6749, f_measure: 0.6579, kappa: 0.1458, kb_relative_information_score: 25.991, mean_absolute_error: 0.3727, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6719, predictive_accuracy: 0.7098, prior_entropy: 0.8796, recall: 0.7098, relative_absolute_error: 0.891, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4394, root_relative_squared_error: 0.9614, scimark_benchmark: 947.7149,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5932, f_measure: 0.7128, kappa: 0.2826, kb_relative_information_score: 38.3101, mean_absolute_error: 0.368, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7524, predictive_accuracy: 0.7552, prior_entropy: 0.8796, recall: 0.7552, relative_absolute_error: 0.8797, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4342, root_relative_squared_error: 0.95, scimark_benchmark: 911.0794,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5708, f_measure: 0.6697, kappa: 0.1802, kb_relative_information_score: 87.9979, mean_absolute_error: 0.2692, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7191, predictive_accuracy: 0.7308, prior_entropy: 0.8796, recall: 0.7308, relative_absolute_error: 0.6436, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5189, root_relative_squared_error: 1.1353, scimark_benchmark: 912.8235,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6594, f_measure: 0.6817, kappa: 0.2055, kb_relative_information_score: 30.7652, mean_absolute_error: 0.3628, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6804, predictive_accuracy: 0.7098, prior_entropy: 0.8796, recall: 0.7098, relative_absolute_error: 0.8674, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4437, root_relative_squared_error: 0.9709, scimark_benchmark: 946.4216,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5229, f_measure: 0.6159, kappa: 0.061, kb_relative_information_score: 70.049, mean_absolute_error: 0.2937, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.663, predictive_accuracy: 0.7063, prior_entropy: 0.8796, recall: 0.7063, relative_absolute_error: 0.7021, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5419, root_relative_squared_error: 1.1858, scimark_benchmark: 925.8431,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5591, f_measure: 0.6496, kappa: 0.1306, kb_relative_information_score: 44.4077, mean_absolute_error: 0.3287, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6407, predictive_accuracy: 0.6713, prior_entropy: 0.8796, recall: 0.6713, relative_absolute_error: 0.7857, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5733, root_relative_squared_error: 1.2544, scimark_benchmark: 950.4319,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6741, f_measure: 0.6512, kappa: 0.1313, kb_relative_information_score: 26.3984, mean_absolute_error: 0.3727, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6709, predictive_accuracy: 0.7098, prior_entropy: 0.8796, recall: 0.7098, relative_absolute_error: 0.891, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4398, root_relative_squared_error: 0.9622, scimark_benchmark: 943.8552,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4831, f_measure: 0.5801, kb_relative_information_score: -0.3776, mean_absolute_error: 0.4184, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.4939, predictive_accuracy: 0.7028, prior_entropy: 0.8796, recall: 0.7028, relative_absolute_error: 1.0002, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4571, root_relative_squared_error: 1.0001, scimark_benchmark: 948.6889,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6627, f_measure: 0.6748, kappa: 0.1945, kb_relative_information_score: 39.7972, mean_absolute_error: 0.3562, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7408, predictive_accuracy: 0.7378, prior_entropy: 0.8796, recall: 0.7378, relative_absolute_error: 0.8515, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4368, root_relative_squared_error: 0.9557, scimark_benchmark: 904.1611,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6951, f_measure: 0.7196, kappa: 0.3146, kb_relative_information_score: 54.5395, mean_absolute_error: 0.3281, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7157, predictive_accuracy: 0.7273, prior_entropy: 0.8796, recall: 0.7273, relative_absolute_error: 0.7843, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4553, root_relative_squared_error: 0.9962, scimark_benchmark: 2010.8275,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.581, f_measure: 0.6934, kappa: 0.2365, kb_relative_information_score: 30.5653, mean_absolute_error: 0.3735, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6916, predictive_accuracy: 0.7168, prior_entropy: 0.8796, recall: 0.7168, relative_absolute_error: 0.893, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4528, root_relative_squared_error: 0.9908, scimark_benchmark: 2007.2153,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5276, f_measure: 0.6266, kappa: 0.0663, kb_relative_information_score: 46.9718, mean_absolute_error: 0.3252, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6188, predictive_accuracy: 0.6748, prior_entropy: 0.8796, recall: 0.6748, relative_absolute_error: 0.7773, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5702, root_relative_squared_error: 1.2477, scimark_benchmark: 2017.3399,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6384, f_measure: 0.6538, kappa: 0.1341, kb_relative_information_score: 21.2816, mean_absolute_error: 0.3743, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6512, predictive_accuracy: 0.6923, prior_entropy: 0.8796, recall: 0.6923, relative_absolute_error: 0.8947, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4551, root_relative_squared_error: 0.9958, scimark_benchmark: 2018.4112,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5834, f_measure: 0.7072, kappa: 0.2682, kb_relative_information_score: 34.8021, mean_absolute_error: 0.3726, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7377, predictive_accuracy: 0.7483, prior_entropy: 0.8796, recall: 0.7483, relative_absolute_error: 0.8907, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4443, root_relative_squared_error: 0.9722, scimark_benchmark: 2005.5064,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6271, f_measure: 0.6802, kappa: 0.2101, kb_relative_information_score: 26.5157, mean_absolute_error: 0.3684, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.674, predictive_accuracy: 0.6958, prior_entropy: 0.8796, recall: 0.6958, relative_absolute_error: 0.8807, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4626, root_relative_squared_error: 1.0121, scimark_benchmark: 2006.3479,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6108, f_measure: 0.6634, kappa: 0.1739, kb_relative_information_score: 41.8549, mean_absolute_error: 0.3356, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6566, predictive_accuracy: 0.6748, prior_entropy: 0.8796, recall: 0.6748, relative_absolute_error: 0.8024, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5459, root_relative_squared_error: 1.1945, scimark_benchmark: 2008.001,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.5801, kb_relative_information_score: 67.4849, mean_absolute_error: 0.2972, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.4939, predictive_accuracy: 0.7028, prior_entropy: 0.8796, recall: 0.7028, relative_absolute_error: 0.7105, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5452, root_relative_squared_error: 1.1928, scimark_benchmark: 2002.8956,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4831, f_measure: 0.5801, kb_relative_information_score: -0.3776, mean_absolute_error: 0.4184, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.4939, predictive_accuracy: 0.7028, prior_entropy: 0.8796, recall: 0.7028, relative_absolute_error: 1.0002, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4571, root_relative_squared_error: 1.0001, scimark_benchmark: 1981.4554,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6798, f_measure: 0.7117, kappa: 0.2788, kb_relative_information_score: 46.3695, mean_absolute_error: 0.3494, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7274, predictive_accuracy: 0.7448, prior_entropy: 0.8796, recall: 0.7448, relative_absolute_error: 0.8353, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4279, root_relative_squared_error: 0.9363, scimark_benchmark: 941.1815,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6587, f_measure: 0.6665, kappa: 0.1738, kb_relative_information_score: 29.6101, mean_absolute_error: 0.3625, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6592, predictive_accuracy: 0.6853, prior_entropy: 0.8796, recall: 0.6853, relative_absolute_error: 0.8666, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.451, root_relative_squared_error: 0.9869, scimark_benchmark: 922.2881,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6678, f_measure: 0.6936, kappa: 0.2549, kb_relative_information_score: 59.9535, mean_absolute_error: 0.3084, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6895, predictive_accuracy: 0.6993, prior_entropy: 0.8796, recall: 0.6993, relative_absolute_error: 0.7372, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.5159, root_relative_squared_error: 1.1287, scimark_benchmark: 931.98,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6487, f_measure: 0.6869, kappa: 0.2231, kb_relative_information_score: 45.3021, mean_absolute_error: 0.3358, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.6821, predictive_accuracy: 0.7063, prior_entropy: 0.8796, recall: 0.7063, relative_absolute_error: 0.8028, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4766, root_relative_squared_error: 1.0428, scimark_benchmark: 941.6697,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6444, f_measure: 0.6989, kappa: 0.247, kb_relative_information_score: 41.5907, mean_absolute_error: 0.3585, mean_prior_absolute_error: 0.4183, number_of_instances: 286, precision: 0.7184, predictive_accuracy: 0.7378, prior_entropy: 0.8796, recall: 0.7378, relative_absolute_error: 0.8569, root_mean_prior_squared_error: 0.457, root_mean_squared_error: 0.4355, root_relative_squared_error: 0.9528, scimark_benchmark: 928.7283,

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