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Function: require_once

OpenML
Task
Supervised Classification on analcatdata_asbestos

Supervised Classification on analcatdata_asbestos

Task 3550 Supervised Classification analcatdata_asbestos 485 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 study_73 under100k under1m
Issue #Downvotes for this reason By


Metric:

485 Runs

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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7923, f_measure: 0.7824, kappa: 0.5588, kb_relative_information_score: 33.9641, mean_absolute_error: 0.3039, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7826, predictive_accuracy: 0.7831, prior_entropy: 0.9919, recall: 0.7831, relative_absolute_error: 0.6149, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4386, root_relative_squared_error: 0.8823, scimark_benchmark: 869.6028,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6945, f_measure: 0.6987, kappa: 0.4095, kb_relative_information_score: 36.2144, mean_absolute_error: 0.2771, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7714, predictive_accuracy: 0.7229, prior_entropy: 0.9919, recall: 0.7229, relative_absolute_error: 0.5607, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.5264, root_relative_squared_error: 1.0591, scimark_benchmark: 1363.454,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5053, f_measure: 0.4337, kappa: 0.0116, kb_relative_information_score: 7.7561, mean_absolute_error: 0.4458, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.5316, predictive_accuracy: 0.5542, prior_entropy: 0.9919, recall: 0.5542, relative_absolute_error: 0.9019, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.6677, root_relative_squared_error: 1.3433, scimark_benchmark: 887.6719,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7911, f_measure: 0.7834, kappa: 0.5702, kb_relative_information_score: 46.378, mean_absolute_error: 0.2169, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7983, predictive_accuracy: 0.7831, prior_entropy: 0.9919, recall: 0.7831, relative_absolute_error: 0.4388, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4657, root_relative_squared_error: 0.9369, scimark_benchmark: 1301.9956,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8088, f_measure: 0.7718, kappa: 0.5428, kb_relative_information_score: 31.1477, mean_absolute_error: 0.3242, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.778, predictive_accuracy: 0.7711, prior_entropy: 0.9919, recall: 0.7711, relative_absolute_error: 0.6559, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4182, root_relative_squared_error: 0.8413, scimark_benchmark: 938.343, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.626, f_measure: 0.6275, kappa: 0.2501, kb_relative_information_score: 19.9525, mean_absolute_error: 0.3735, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.6301, predictive_accuracy: 0.6265, prior_entropy: 0.9919, recall: 0.6265, relative_absolute_error: 0.7557, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.6111, root_relative_squared_error: 1.2295, scimark_benchmark: 939.449, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8373, f_measure: 0.7954, kappa: 0.5866, kb_relative_information_score: 33.862, mean_absolute_error: 0.3079, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7959, predictive_accuracy: 0.7952, prior_entropy: 0.9919, recall: 0.7952, relative_absolute_error: 0.6229, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4037, root_relative_squared_error: 0.8121, scimark_benchmark: 917.9672, usercpu_time_millis: 50, usercpu_time_millis_testing: 10, usercpu_time_millis_training: 40,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8064, f_measure: 0.7356, kappa: 0.4719, kb_relative_information_score: 32.654, mean_absolute_error: 0.3092, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.744, predictive_accuracy: 0.7349, prior_entropy: 0.9919, recall: 0.7349, relative_absolute_error: 0.6256, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4256, root_relative_squared_error: 0.8562, scimark_benchmark: 1319.6463,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8135, f_measure: 0.7815, kappa: 0.5564, kb_relative_information_score: 40.9641, mean_absolute_error: 0.2586, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7835, predictive_accuracy: 0.7831, prior_entropy: 0.9919, recall: 0.7831, relative_absolute_error: 0.5232, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4137, root_relative_squared_error: 0.8323, scimark_benchmark: 1346.9602, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7867, f_measure: 0.7815, kappa: 0.5564, kb_relative_information_score: 47.4026, mean_absolute_error: 0.2108, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7835, predictive_accuracy: 0.7831, prior_entropy: 0.9919, recall: 0.7831, relative_absolute_error: 0.4266, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4559, root_relative_squared_error: 0.9172, scimark_benchmark: 1280.6952,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7565, f_measure: 0.7597, kappa: 0.52, kb_relative_information_score: 29.4065, mean_absolute_error: 0.3336, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7682, predictive_accuracy: 0.759, prior_entropy: 0.9919, recall: 0.759, relative_absolute_error: 0.6749, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4261, root_relative_squared_error: 0.8573, scimark_benchmark: 1306.6379,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7576, f_measure: 0.7475, kappa: 0.4973, kb_relative_information_score: 28.5822, mean_absolute_error: 0.3393, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7587, predictive_accuracy: 0.747, prior_entropy: 0.9919, recall: 0.747, relative_absolute_error: 0.6864, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4319, root_relative_squared_error: 0.8688, scimark_benchmark: 1376.7478,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7618, f_measure: 0.7687, kappa: 0.5305, kb_relative_information_score: 44.3453, mean_absolute_error: 0.2289, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7721, predictive_accuracy: 0.7711, prior_entropy: 0.9919, recall: 0.7711, relative_absolute_error: 0.4631, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4785, root_relative_squared_error: 0.9626, scimark_benchmark: 1359.379,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8067, f_measure: 0.8076, kappa: 0.6119, kb_relative_information_score: 47.8393, mean_absolute_error: 0.211, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.809, predictive_accuracy: 0.8072, prior_entropy: 0.9919, recall: 0.8072, relative_absolute_error: 0.4269, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4408, root_relative_squared_error: 0.8868, scimark_benchmark: 933.1776, usercpu_time_millis: 700, usercpu_time_millis_training: 700,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7212, f_measure: 0.7295, kappa: 0.452, kb_relative_information_score: 38.2471, mean_absolute_error: 0.2651, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7384, predictive_accuracy: 0.7349, prior_entropy: 0.9919, recall: 0.7349, relative_absolute_error: 0.5363, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.5148, root_relative_squared_error: 1.0358, scimark_benchmark: 1339.4189,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7165, f_measure: 0.7108, kappa: 0.4148, kb_relative_information_score: 26.7934, mean_absolute_error: 0.3454, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7108, predictive_accuracy: 0.7108, prior_entropy: 0.9919, recall: 0.7108, relative_absolute_error: 0.6989, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4743, root_relative_squared_error: 0.9542, scimark_benchmark: 1339.4189,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6613, f_measure: 0.6633, kappa: 0.3209, kb_relative_information_score: 26.0507, mean_absolute_error: 0.3373, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.6648, predictive_accuracy: 0.6627, prior_entropy: 0.9919, recall: 0.6627, relative_absolute_error: 0.6825, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.5808, root_relative_squared_error: 1.1685, scimark_benchmark: 1351.788,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6343, f_measure: 0.6386, kappa: 0.2685, kb_relative_information_score: 21.9852, mean_absolute_error: 0.3614, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.6386, predictive_accuracy: 0.6386, prior_entropy: 0.9919, recall: 0.6386, relative_absolute_error: 0.7313, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.6012, root_relative_squared_error: 1.2095, scimark_benchmark: 1505.597,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4445, f_measure: 0.3953, kb_relative_information_score: -0.1479, mean_absolute_error: 0.4948, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.3072, predictive_accuracy: 0.5542, prior_entropy: 0.9919, recall: 0.5542, relative_absolute_error: 1.0011, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4976, root_relative_squared_error: 1.0011, scimark_benchmark: 1505.597,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7156, f_measure: 0.7236, kappa: 0.4436, kb_relative_information_score: 28.7205, mean_absolute_error: 0.3343, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7262, predictive_accuracy: 0.7229, prior_entropy: 0.9919, recall: 0.7229, relative_absolute_error: 0.6763, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4707, root_relative_squared_error: 0.947, scimark_benchmark: 1339.4189,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.785, f_measure: 0.7837, kappa: 0.568, kb_relative_information_score: 36.3974, mean_absolute_error: 0.2926, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7925, predictive_accuracy: 0.7831, prior_entropy: 0.9919, recall: 0.7831, relative_absolute_error: 0.592, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4142, root_relative_squared_error: 0.8333, scimark_benchmark: 942.6953,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7814, f_measure: 0.7837, kappa: 0.568, kb_relative_information_score: 36.0676, mean_absolute_error: 0.2945, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7925, predictive_accuracy: 0.7831, prior_entropy: 0.9919, recall: 0.7831, relative_absolute_error: 0.5959, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4155, root_relative_squared_error: 0.8359, scimark_benchmark: 926.6717,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7814, f_measure: 0.7837, kappa: 0.568, kb_relative_information_score: 36.0676, mean_absolute_error: 0.2945, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7925, predictive_accuracy: 0.7831, prior_entropy: 0.9919, recall: 0.7831, relative_absolute_error: 0.5959, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4155, root_relative_squared_error: 0.8359, scimark_benchmark: 936.7115, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8129, f_measure: 0.7718, kappa: 0.5428, kb_relative_information_score: 42.632, mean_absolute_error: 0.2404, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.778, predictive_accuracy: 0.7711, prior_entropy: 0.9919, recall: 0.7711, relative_absolute_error: 0.4864, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4607, root_relative_squared_error: 0.9268, scimark_benchmark: 894.7455, usercpu_time_millis: 20, usercpu_time_millis_training: 20,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8234, f_measure: 0.7597, kappa: 0.5174, kb_relative_information_score: 43.4413, mean_absolute_error: 0.2357, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.764, predictive_accuracy: 0.759, prior_entropy: 0.9919, recall: 0.759, relative_absolute_error: 0.4769, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4453, root_relative_squared_error: 0.8959, scimark_benchmark: 936.6206, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8252, f_measure: 0.7716, kappa: 0.5451, kb_relative_information_score: 43.2424, mean_absolute_error: 0.237, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.783, predictive_accuracy: 0.7711, prior_entropy: 0.9919, recall: 0.7711, relative_absolute_error: 0.4794, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4547, root_relative_squared_error: 0.9148, scimark_benchmark: 911.3823, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8255, f_measure: 0.7477, kappa: 0.4946, kb_relative_information_score: 41.8482, mean_absolute_error: 0.2436, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7539, predictive_accuracy: 0.747, prior_entropy: 0.9919, recall: 0.747, relative_absolute_error: 0.4928, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4382, root_relative_squared_error: 0.8816, scimark_benchmark: 933.8635, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8264, f_measure: 0.7475, kappa: 0.4973, kb_relative_information_score: 41.3279, mean_absolute_error: 0.2471, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7587, predictive_accuracy: 0.747, prior_entropy: 0.9919, recall: 0.747, relative_absolute_error: 0.5, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.452, root_relative_squared_error: 0.9093, scimark_benchmark: 938.9865,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7644, f_measure: 0.7466, kappa: 0.4866, kb_relative_information_score: 34.2169, mean_absolute_error: 0.2948, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7465, predictive_accuracy: 0.747, prior_entropy: 0.9919, recall: 0.747, relative_absolute_error: 0.5965, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4726, root_relative_squared_error: 0.9508, 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: 0.7814, f_measure: 0.7583, kappa: 0.5097, kb_relative_information_score: 34.5377, mean_absolute_error: 0.2955, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7584, predictive_accuracy: 0.759, prior_entropy: 0.9919, recall: 0.759, relative_absolute_error: 0.5979, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4462, root_relative_squared_error: 0.8977, scimark_benchmark: 938.2848, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7967, f_measure: 0.8066, kappa: 0.6078, kb_relative_information_score: 36.5034, mean_absolute_error: 0.2888, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.8069, predictive_accuracy: 0.8072, prior_entropy: 0.9919, recall: 0.8072, relative_absolute_error: 0.5843, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4226, root_relative_squared_error: 0.8501, scimark_benchmark: 924.6116,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8046, f_measure: 0.7954, kappa: 0.5866, kb_relative_information_score: 34.7855, mean_absolute_error: 0.3003, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7959, predictive_accuracy: 0.7952, prior_entropy: 0.9919, recall: 0.7952, relative_absolute_error: 0.6077, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4173, root_relative_squared_error: 0.8394, scimark_benchmark: 902.4773, usercpu_time_millis: 10, usercpu_time_millis_training: 10,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8082, f_measure: 0.7714, kappa: 0.5379, kb_relative_information_score: 34.2071, mean_absolute_error: 0.3027, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7718, predictive_accuracy: 0.7711, prior_entropy: 0.9919, recall: 0.7711, relative_absolute_error: 0.6124, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4141, root_relative_squared_error: 0.833, scimark_benchmark: 902.4773,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4445, f_measure: 0.3953, kb_relative_information_score: -0.1479, mean_absolute_error: 0.4948, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.3072, predictive_accuracy: 0.5542, prior_entropy: 0.9919, recall: 0.5542, relative_absolute_error: 1.0011, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4976, root_relative_squared_error: 1.0011, scimark_benchmark: 932.3943,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8367, f_measure: 0.7717, kappa: 0.5404, kb_relative_information_score: 34.3024, mean_absolute_error: 0.3048, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7743, predictive_accuracy: 0.7711, prior_entropy: 0.9919, recall: 0.7711, relative_absolute_error: 0.6166, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4017, root_relative_squared_error: 0.8081, scimark_benchmark: 943.2745, usercpu_time_millis: 10, usercpu_time_millis_testing: 10,
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7444, build_cpu_time: 0.0093, build_memory: 84462081.3494, f_measure: 0.7466, kappa: 0.4866, kb_relative_information_score: 34.1576, mean_absolute_error: 0.2987, mean_prior_absolute_error: 0.4943, number_of_instances: 83, precision: 0.7465, predictive_accuracy: 0.747, prior_entropy: 0.9919, recall: 0.747, relative_absolute_error: 0.6043, root_mean_prior_squared_error: 0.4971, root_mean_squared_error: 0.4545, root_relative_squared_error: 0.9144, scimark_benchmark: 925.481,
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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