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CC18-Example

CC18-Example

Created 26-08-2021 by Pieter Gijsbers Visibility: public
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Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set churn
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6065, f_measure: 0.8476, kappa: 0.3017, kb_relative_information_score: 0.1019, mean_absolute_error: 0.2156, mean_prior_absolute_error: 0.2429, weighted_recall: 0.8698, number_of_instances: 5000, precision: 0.8463, predictive_accuracy: 0.8698, prior_entropy: 0.5879, relative_absolute_error: 0.8875, root_mean_prior_squared_error: 0.3484, root_mean_squared_error: 0.3287, root_relative_squared_error: 0.9433, unweighted_recall: 0.6158,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set dna
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7649, kappa: 0.4117, kb_relative_information_score: 0.3341, mean_absolute_error: 0.3012, mean_prior_absolute_error: 0.41, weighted_recall: 0.6234, number_of_instances: 3186, predictive_accuracy: 0.6234, prior_entropy: 1.4798, relative_absolute_error: 0.7347, root_mean_prior_squared_error: 0.4527, root_mean_squared_error: 0.3881, root_relative_squared_error: 0.8573, unweighted_recall: 0.5789,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set Internet-Advertisements
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6792, f_measure: 0.8902, kappa: 0.4936, kb_relative_information_score: 0.3248, mean_absolute_error: 0.1649, mean_prior_absolute_error: 0.2409, weighted_recall: 0.9058, number_of_instances: 3279, precision: 0.9018, predictive_accuracy: 0.9058, prior_entropy: 0.5842, relative_absolute_error: 0.6845, root_mean_prior_squared_error: 0.347, root_mean_squared_error: 0.2919, root_relative_squared_error: 0.8413, unweighted_recall: 0.6908,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set CIFAR_10
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6019, kappa: 0.0619, kb_relative_information_score: 0.0943, mean_absolute_error: 0.1763, mean_prior_absolute_error: 0.18, weighted_recall: 0.1557, number_of_instances: 60000, predictive_accuracy: 0.1557, prior_entropy: 3.3219, relative_absolute_error: 0.9795, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.297, root_relative_squared_error: 0.9899, unweighted_recall: 0.1557,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set Devnagari-Script
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5534, kappa: 0.0187, kb_relative_information_score: 0.0457, mean_absolute_error: 0.0421, mean_prior_absolute_error: 0.0425, weighted_recall: 0.04, number_of_instances: 92000, predictive_accuracy: 0.04, prior_entropy: 5.5236, relative_absolute_error: 0.9908, root_mean_prior_squared_error: 0.1458, root_mean_squared_error: 0.1452, root_relative_squared_error: 0.9954, unweighted_recall: 0.04,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set numerai28.6
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5119, f_measure: 0.4903, kappa: 0.0238, kb_relative_information_score: 0.0012, mean_absolute_error: 0.4995, mean_prior_absolute_error: 0.4999, weighted_recall: 0.5141, number_of_instances: 96320, precision: 0.5146, predictive_accuracy: 0.5141, prior_entropy: 0.9999, relative_absolute_error: 0.9991, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.4998, root_relative_squared_error: 0.9997, unweighted_recall: 0.5118,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set jungle_chess_2pcs_raw_endgame_complete
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6795, kappa: 0.3176, kb_relative_information_score: 0.1738, mean_absolute_error: 0.3348, mean_prior_absolute_error: 0.3832, weighted_recall: 0.6159, number_of_instances: 44819, predictive_accuracy: 0.6159, prior_entropy: 1.3491, relative_absolute_error: 0.8737, root_mean_prior_squared_error: 0.4377, root_mean_squared_error: 0.4092, root_relative_squared_error: 0.9348, unweighted_recall: 0.4648,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set Fashion-MNIST
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.709, kappa: 0.1103, kb_relative_information_score: 0.2329, mean_absolute_error: 0.1637, mean_prior_absolute_error: 0.18, weighted_recall: 0.1992, number_of_instances: 70000, predictive_accuracy: 0.1992, prior_entropy: 3.3219, relative_absolute_error: 0.9095, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2861, root_relative_squared_error: 0.9537, unweighted_recall: 0.1992,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set mfeat-pixel
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7076, kappa: 0.1078, kb_relative_information_score: 0.23, mean_absolute_error: 0.1661, mean_prior_absolute_error: 0.18, weighted_recall: 0.197, number_of_instances: 2000, predictive_accuracy: 0.197, prior_entropy: 3.3219, relative_absolute_error: 0.9229, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2883, root_relative_squared_error: 0.9611, unweighted_recall: 0.197,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set segment
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6428, kappa: 0.1667, kb_relative_information_score: 0.2107, mean_absolute_error: 0.2041, mean_prior_absolute_error: 0.2449, weighted_recall: 0.2857, number_of_instances: 2310, predictive_accuracy: 0.2857, prior_entropy: 2.8074, relative_absolute_error: 0.8334, root_mean_prior_squared_error: 0.3499, root_mean_squared_error: 0.3196, root_relative_squared_error: 0.9132, unweighted_recall: 0.2857,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set car
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7069, kb_relative_information_score: 0.0974, mean_absolute_error: 0.1963, mean_prior_absolute_error: 0.229, weighted_recall: 0.7002, number_of_instances: 1728, predictive_accuracy: 0.7002, prior_entropy: 1.2058, relative_absolute_error: 0.8575, root_mean_prior_squared_error: 0.3381, root_mean_squared_error: 0.3125, root_relative_squared_error: 0.9244, unweighted_recall: 0.25,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set wilt
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6637, kb_relative_information_score: -0.8238, mean_absolute_error: 0.099, mean_prior_absolute_error: 0.1022, weighted_recall: 0.9461, number_of_instances: 4839, predictive_accuracy: 0.9461, prior_entropy: 0.3029, relative_absolute_error: 0.9687, root_mean_prior_squared_error: 0.2259, root_mean_squared_error: 0.2226, root_relative_squared_error: 0.9852, unweighted_recall: 0.5,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set climate-model-simulation-crashes
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6532, kb_relative_information_score: -0.4844, mean_absolute_error: 0.1497, mean_prior_absolute_error: 0.1571, weighted_recall: 0.9148, number_of_instances: 540, predictive_accuracy: 0.9148, prior_entropy: 0.4202, relative_absolute_error: 0.9525, root_mean_prior_squared_error: 0.2792, root_mean_squared_error: 0.2783, root_relative_squared_error: 0.9969, unweighted_recall: 0.5,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set steel-plates-fault
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6495, kappa: 0.2567, kb_relative_information_score: 0.2217, mean_absolute_error: 0.1826, mean_prior_absolute_error: 0.2223, weighted_recall: 0.4956, number_of_instances: 1941, predictive_accuracy: 0.4956, prior_entropy: 2.4107, relative_absolute_error: 0.8212, root_mean_prior_squared_error: 0.3334, root_mean_squared_error: 0.3023, root_relative_squared_error: 0.9068, unweighted_recall: 0.252,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set MiceProtein
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.771, kappa: 0.1548, kb_relative_information_score: 0.3235, mean_absolute_error: 0.1885, mean_prior_absolute_error: 0.2185, weighted_recall: 0.2722, number_of_instances: 1080, predictive_accuracy: 0.2722, prior_entropy: 2.993, relative_absolute_error: 0.8628, root_mean_prior_squared_error: 0.3305, root_mean_squared_error: 0.3071, root_relative_squared_error: 0.9292, unweighted_recall: 0.245,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set connect-4
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5739, kb_relative_information_score: 0.0252, mean_absolute_error: 0.3241, mean_prior_absolute_error: 0.3313, weighted_recall: 0.6583, number_of_instances: 67557, predictive_accuracy: 0.6583, prior_entropy: 1.2184, relative_absolute_error: 0.9783, root_mean_prior_squared_error: 0.407, root_mean_squared_error: 0.4026, root_relative_squared_error: 0.9893, unweighted_recall: 0.3333,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set texture
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5889, kappa: 0.0982, kb_relative_information_score: 0.1237, mean_absolute_error: 0.1495, mean_prior_absolute_error: 0.1653, weighted_recall: 0.1802, number_of_instances: 5500, predictive_accuracy: 0.1802, prior_entropy: 3.4594, relative_absolute_error: 0.9048, root_mean_prior_squared_error: 0.2875, root_mean_squared_error: 0.2736, root_relative_squared_error: 0.9518, unweighted_recall: 0.1802,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set dresses-sales
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5608, f_measure: 0.6075, kappa: 0.1983, kb_relative_information_score: 0.0568, mean_absolute_error: 0.4656, mean_prior_absolute_error: 0.4873, weighted_recall: 0.632, number_of_instances: 500, precision: 0.6261, predictive_accuracy: 0.632, prior_entropy: 0.9815, relative_absolute_error: 0.9556, root_mean_prior_squared_error: 0.4936, root_mean_squared_error: 0.4831, root_relative_squared_error: 0.9788, unweighted_recall: 0.5934,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set har
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6855, kappa: 0.231, kb_relative_information_score: 0.2713, mean_absolute_error: 0.2157, mean_prior_absolute_error: 0.2771, weighted_recall: 0.3738, number_of_instances: 10299, predictive_accuracy: 0.3738, prior_entropy: 2.5759, relative_absolute_error: 0.7785, root_mean_prior_squared_error: 0.3722, root_mean_squared_error: 0.3284, root_relative_squared_error: 0.8824, unweighted_recall: 0.3333,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set GesturePhaseSegmentationProcessed
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.648, kappa: 0.2147, kb_relative_information_score: 0.1635, mean_absolute_error: 0.2808, mean_prior_absolute_error: 0.3065, weighted_recall: 0.4438, number_of_instances: 9873, predictive_accuracy: 0.4438, prior_entropy: 2.1934, relative_absolute_error: 0.916, root_mean_prior_squared_error: 0.3915, root_mean_squared_error: 0.3749, root_relative_squared_error: 0.9577, unweighted_recall: 0.3066,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set bank-marketing
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6732, kb_relative_information_score: 0.0315, mean_absolute_error: 0.1813, mean_prior_absolute_error: 0.2066, weighted_recall: 0.883, number_of_instances: 45211, predictive_accuracy: 0.883, prior_entropy: 0.5206, relative_absolute_error: 0.8774, root_mean_prior_squared_error: 0.3214, root_mean_squared_error: 0.3014, root_relative_squared_error: 0.9377, unweighted_recall: 0.5,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set cylinder-bands
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5931, f_measure: 0.6165, kappa: 0.2687, kb_relative_information_score: 0.1693, mean_absolute_error: 0.4123, mean_prior_absolute_error: 0.4879, weighted_recall: 0.6796, number_of_instances: 540, precision: 0.7939, predictive_accuracy: 0.6796, prior_entropy: 0.9825, relative_absolute_error: 0.845, root_mean_prior_squared_error: 0.4939, root_mean_squared_error: 0.4542, root_relative_squared_error: 0.9196, unweighted_recall: 0.6206,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set PhishingWebsites
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.88, f_measure: 0.8887, kappa: 0.7741, kb_relative_information_score: 0.6402, mean_absolute_error: 0.1975, mean_prior_absolute_error: 0.4935, weighted_recall: 0.8889, number_of_instances: 11055, precision: 0.8889, predictive_accuracy: 0.8889, prior_entropy: 0.9906, relative_absolute_error: 0.4002, root_mean_prior_squared_error: 0.4967, root_mean_squared_error: 0.3143, root_relative_squared_error: 0.6326, unweighted_recall: 0.8856,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set blood-transfusion-service-center
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6592, kb_relative_information_score: 0.0135, mean_absolute_error: 0.3276, mean_prior_absolute_error: 0.363, weighted_recall: 0.762, number_of_instances: 748, predictive_accuracy: 0.762, prior_entropy: 0.7916, relative_absolute_error: 0.9025, root_mean_prior_squared_error: 0.4258, root_mean_squared_error: 0.4049, root_relative_squared_error: 0.9508, unweighted_recall: 0.5,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set banknote-authentication
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8331, f_measure: 0.8532, kappa: 0.7041, kb_relative_information_score: 0.5415, mean_absolute_error: 0.2476, mean_prior_absolute_error: 0.4939, weighted_recall: 0.8528, number_of_instances: 1372, precision: 0.8555, predictive_accuracy: 0.8528, prior_entropy: 0.9911, relative_absolute_error: 0.5014, root_mean_prior_squared_error: 0.4969, root_mean_squared_error: 0.3526, root_relative_squared_error: 0.7095, unweighted_recall: 0.8547,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set first-order-theorem-proving
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5601, kb_relative_information_score: 0.0523, mean_absolute_error: 0.2427, mean_prior_absolute_error: 0.2508, weighted_recall: 0.4175, number_of_instances: 6118, predictive_accuracy: 0.4175, prior_entropy: 2.3, relative_absolute_error: 0.9678, root_mean_prior_squared_error: 0.3541, root_mean_squared_error: 0.3484, root_relative_squared_error: 0.9839, unweighted_recall: 0.1667,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set cnae-9
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.591, kappa: 0.1063, kb_relative_information_score: 0.1333, mean_absolute_error: 0.1772, mean_prior_absolute_error: 0.1975, weighted_recall: 0.2056, number_of_instances: 1080, predictive_accuracy: 0.2056, prior_entropy: 3.1699, relative_absolute_error: 0.8969, root_mean_prior_squared_error: 0.3143, root_mean_squared_error: 0.2976, root_relative_squared_error: 0.9471, unweighted_recall: 0.2056,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set ozone-level-8hr
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7206, kb_relative_information_score: -0.6491, mean_absolute_error: 0.1115, mean_prior_absolute_error: 0.1186, weighted_recall: 0.9369, number_of_instances: 2534, predictive_accuracy: 0.9369, prior_entropy: 0.3398, relative_absolute_error: 0.9399, root_mean_prior_squared_error: 0.2432, root_mean_squared_error: 0.2373, root_relative_squared_error: 0.9757, unweighted_recall: 0.5,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set nomao
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8348, f_measure: 0.855, kappa: 0.6497, kb_relative_information_score: 0.4488, mean_absolute_error: 0.2344, mean_prior_absolute_error: 0.4081, weighted_recall: 0.8532, number_of_instances: 34465, precision: 0.8581, predictive_accuracy: 0.8532, prior_entropy: 0.863, relative_absolute_error: 0.5745, root_mean_prior_squared_error: 0.4517, root_mean_squared_error: 0.3429, root_relative_squared_error: 0.7591, unweighted_recall: 0.8336,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set madelon
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6064, f_measure: 0.6161, kappa: 0.2331, kb_relative_information_score: 0.0727, mean_absolute_error: 0.4719, mean_prior_absolute_error: 0.5, weighted_recall: 0.6165, number_of_instances: 2600, precision: 0.6171, predictive_accuracy: 0.6165, prior_entropy: 1, relative_absolute_error: 0.9437, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.4864, root_relative_squared_error: 0.9728, unweighted_recall: 0.6165,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set ilpd
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6461, kb_relative_information_score: 0.027, mean_absolute_error: 0.3724, mean_prior_absolute_error: 0.4091, weighted_recall: 0.7136, number_of_instances: 583, predictive_accuracy: 0.7136, prior_entropy: 0.8641, relative_absolute_error: 0.9102, root_mean_prior_squared_error: 0.4521, root_mean_squared_error: 0.4331, root_relative_squared_error: 0.958, unweighted_recall: 0.5,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set semeion
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6733, kappa: 0.106, kb_relative_information_score: 0.1769, mean_absolute_error: 0.1689, mean_prior_absolute_error: 0.18, weighted_recall: 0.1959, number_of_instances: 1593, predictive_accuracy: 0.1959, prior_entropy: 3.3218, relative_absolute_error: 0.9382, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.291, root_relative_squared_error: 0.9699, unweighted_recall: 0.1947,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set wall-robot-navigation
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7756, kappa: 0.5008, kb_relative_information_score: 0.3926, mean_absolute_error: 0.212, mean_prior_absolute_error: 0.3312, weighted_recall: 0.6996, number_of_instances: 5456, predictive_accuracy: 0.6996, prior_entropy: 1.7146, relative_absolute_error: 0.6401, root_mean_prior_squared_error: 0.4069, root_mean_squared_error: 0.3256, root_relative_squared_error: 0.8002, unweighted_recall: 0.4422,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set qsar-biodeg
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7352, f_measure: 0.7421, kappa: 0.4437, kb_relative_information_score: 0.2271, mean_absolute_error: 0.3504, mean_prior_absolute_error: 0.4472, weighted_recall: 0.7365, number_of_instances: 1055, precision: 0.7573, predictive_accuracy: 0.7365, prior_entropy: 0.9223, relative_absolute_error: 0.7835, root_mean_prior_squared_error: 0.4728, root_mean_squared_error: 0.4222, root_relative_squared_error: 0.8929, unweighted_recall: 0.735,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set phoneme
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7345, f_measure: 0.7603, kappa: 0.4471, kb_relative_information_score: 0.2071, mean_absolute_error: 0.3277, mean_prior_absolute_error: 0.4147, weighted_recall: 0.7531, number_of_instances: 5404, precision: 0.7764, predictive_accuracy: 0.7531, prior_entropy: 0.8732, relative_absolute_error: 0.7902, root_mean_prior_squared_error: 0.4554, root_mean_squared_error: 0.4058, root_relative_squared_error: 0.8912, unweighted_recall: 0.7407,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set wdbc
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8694, f_measure: 0.8877, kappa: 0.7574, kb_relative_information_score: 0.6643, mean_absolute_error: 0.169, mean_prior_absolute_error: 0.4676, weighted_recall: 0.8893, number_of_instances: 569, precision: 0.8899, predictive_accuracy: 0.8893, prior_entropy: 0.9526, relative_absolute_error: 0.3613, root_mean_prior_squared_error: 0.4835, root_mean_squared_error: 0.3151, root_relative_squared_error: 0.6518, unweighted_recall: 0.8696,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set Bioresponse
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.725, f_measure: 0.7372, kappa: 0.4719, kb_relative_information_score: 0.2618, mean_absolute_error: 0.3858, mean_prior_absolute_error: 0.4964, weighted_recall: 0.7369, number_of_instances: 3751, precision: 0.7383, predictive_accuracy: 0.7369, prior_entropy: 0.9948, relative_absolute_error: 0.7771, root_mean_prior_squared_error: 0.4982, root_mean_squared_error: 0.4393, root_relative_squared_error: 0.8817, unweighted_recall: 0.7368,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set adult
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7556, kb_relative_information_score: 0.1217, mean_absolute_error: 0.2917, mean_prior_absolute_error: 0.3641, weighted_recall: 0.7607, number_of_instances: 48842, predictive_accuracy: 0.7607, prior_entropy: 0.7938, relative_absolute_error: 0.8012, root_mean_prior_squared_error: 0.4266, root_mean_squared_error: 0.3819, root_relative_squared_error: 0.8951, unweighted_recall: 0.5,
Pieter Gijsbers 3 years ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(1) on task Supervised Classification on data set kc2
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7253, f_measure: 0.8494, kappa: 0.5404, kb_relative_information_score: 0.2884, mean_absolute_error: 0.2301, mean_prior_absolute_error: 0.3266, weighted_recall: 0.8487, number_of_instances: 522, precision: 0.8503, predictive_accuracy: 0.8487, prior_entropy: 0.7318, relative_absolute_error: 0.7046, root_mean_prior_squared_error: 0.4037, root_mean_squared_error: 0.3414, root_relative_squared_error: 0.8456, unweighted_recall: 0.773,