0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9669, f_measure: 0.7413, kappa: 0.7228, kb_relative_information_score: 0.6241, mean_absolute_error: 0.1045, mean_prior_absolute_error: 0.18, weighted_recall: 0.7505, number_of_instances: 2000, precision: 0.7502, predictive_accuracy: 0.7505, prior_entropy: 3.3219, relative_absolute_error: 0.5803, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2028, root_relative_squared_error: 0.6759, unweighted_recall: 0.7505,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9953, f_measure: 0.9214, kappa: 0.9133, kb_relative_information_score: 0.7005, mean_absolute_error: 0.0927, mean_prior_absolute_error: 0.18, weighted_recall: 0.922, number_of_instances: 2000, precision: 0.9235, predictive_accuracy: 0.922, prior_entropy: 3.3219, relative_absolute_error: 0.5148, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.1732, root_relative_squared_error: 0.5774, unweighted_recall: 0.922,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9375, kappa: 0.7329, kb_relative_information_score: 0.2501, mean_absolute_error: 0.2968, mean_prior_absolute_error: 0.3798, weighted_recall: 0.856, number_of_instances: 625, predictive_accuracy: 0.856, prior_entropy: 1.3181, relative_absolute_error: 0.7815, root_mean_prior_squared_error: 0.4356, root_mean_squared_error: 0.3517, root_relative_squared_error: 0.8074, unweighted_recall: 0.6192,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.921, kappa: 0.5132, kb_relative_information_score: 0.2411, mean_absolute_error: 0.07, mean_prior_absolute_error: 0.074, weighted_recall: 0.5321, number_of_instances: 20000, predictive_accuracy: 0.5321, prior_entropy: 4.6998, relative_absolute_error: 0.9468, root_mean_prior_squared_error: 0.1923, root_mean_squared_error: 0.1835, root_relative_squared_error: 0.9543, unweighted_recall: 0.5289,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9821, f_measure: 0.9424, kappa: 0.885, kb_relative_information_score: 0.8033, mean_absolute_error: 0.1061, mean_prior_absolute_error: 0.499, weighted_recall: 0.9424, number_of_instances: 3196, precision: 0.9446, predictive_accuracy: 0.9424, prior_entropy: 0.9986, relative_absolute_error: 0.2126, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.2236, root_relative_squared_error: 0.4476, unweighted_recall: 0.9438,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9831, f_measure: 0.9407, kappa: 0.8812, kb_relative_information_score: 0.7471, mean_absolute_error: 0.1439, mean_prior_absolute_error: 0.499, weighted_recall: 0.9409, number_of_instances: 3196, precision: 0.9422, predictive_accuracy: 0.9409, prior_entropy: 0.9986, relative_absolute_error: 0.2883, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.2266, root_relative_squared_error: 0.4537, unweighted_recall: 0.9395,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7917, f_measure: 0.7023, kappa: 0.2613, kb_relative_information_score: 0.1565, mean_absolute_error: 0.3552, mean_prior_absolute_error: 0.4202, weighted_recall: 0.745, number_of_instances: 1000, precision: 0.7353, predictive_accuracy: 0.745, prior_entropy: 0.8813, relative_absolute_error: 0.8454, root_mean_prior_squared_error: 0.4583, root_mean_squared_error: 0.4095, root_relative_squared_error: 0.8935, unweighted_recall: 0.6074,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.727, kb_relative_information_score: 0.0687, mean_absolute_error: 0.3252, 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: 0.8957, root_mean_prior_squared_error: 0.4258, root_mean_squared_error: 0.3985, root_relative_squared_error: 0.9358, unweighted_recall: 0.5,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
27 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
0 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
0 runs0 likes0 downloads0 reach0 impact
Principal component analysis (PCA). Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. The input data is centered but not scaled…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9974, f_measure: 0.9746, kappa: 0.9484, kb_relative_information_score: 0.9429, mean_absolute_error: 0.0287, mean_prior_absolute_error: 0.4935, weighted_recall: 0.9746, number_of_instances: 11055, precision: 0.9746, predictive_accuracy: 0.9746, prior_entropy: 0.9906, relative_absolute_error: 0.0582, root_mean_prior_squared_error: 0.4967, root_mean_squared_error: 0.1359, root_relative_squared_error: 0.2736, unweighted_recall: 0.9736,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9867, f_measure: 0.9357, kappa: 0.8695, kb_relative_information_score: 0.0585, mean_absolute_error: 0.473, mean_prior_absolute_error: 0.4935, weighted_recall: 0.9358, number_of_instances: 11055, precision: 0.9359, predictive_accuracy: 0.9358, prior_entropy: 0.9906, relative_absolute_error: 0.9585, root_mean_prior_squared_error: 0.4967, root_mean_squared_error: 0.4735, root_relative_squared_error: 0.9532, unweighted_recall: 0.9336,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7151, kb_relative_information_score: -0.1701, mean_absolute_error: 0.4439, 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: 1.0851, root_mean_prior_squared_error: 0.4521, root_mean_squared_error: 0.4531, root_relative_squared_error: 1.0022, unweighted_recall: 0.5,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7375, f_measure: 0.6625, kappa: 0.1338, kb_relative_information_score: 0.1096, mean_absolute_error: 0.3461, mean_prior_absolute_error: 0.4091, weighted_recall: 0.6998, number_of_instances: 583, precision: 0.6576, predictive_accuracy: 0.6998, prior_entropy: 0.8641, relative_absolute_error: 0.846, root_mean_prior_squared_error: 0.4521, root_mean_squared_error: 0.4196, root_relative_squared_error: 0.9281, unweighted_recall: 0.5567,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8246, f_measure: 0.7562, kappa: 0.4567, kb_relative_information_score: 0.3407, mean_absolute_error: 0.3021, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7604, number_of_instances: 768, precision: 0.7553, predictive_accuracy: 0.7604, prior_entropy: 0.9331, relative_absolute_error: 0.6647, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4022, root_relative_squared_error: 0.8438, unweighted_recall: 0.7216,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8182, f_measure: 0.7601, kappa: 0.4651, kb_relative_information_score: -0.0935, mean_absolute_error: 0.4772, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7643, number_of_instances: 768, precision: 0.7592, predictive_accuracy: 0.7643, prior_entropy: 0.9331, relative_absolute_error: 1.05, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4783, root_relative_squared_error: 1.0035, unweighted_recall: 0.7255,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8304, f_measure: 0.8366, kappa: 0.4637, kb_relative_information_score: -0.4351, mean_absolute_error: 0.379, mean_prior_absolute_error: 0.3266, weighted_recall: 0.8525, number_of_instances: 522, precision: 0.8417, predictive_accuracy: 0.8525, prior_entropy: 0.7318, relative_absolute_error: 1.1603, root_mean_prior_squared_error: 0.4037, root_mean_squared_error: 0.394, root_relative_squared_error: 0.9759, unweighted_recall: 0.6957,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7169, f_measure: 0.801, kappa: 0.3789, kb_relative_information_score: 0.2555, mean_absolute_error: 0.2139, mean_prior_absolute_error: 0.3266, weighted_recall: 0.8046, number_of_instances: 522, precision: 0.798, predictive_accuracy: 0.8046, prior_entropy: 0.7318, relative_absolute_error: 0.655, root_mean_prior_squared_error: 0.4037, root_mean_squared_error: 0.3984, root_relative_squared_error: 0.9869, unweighted_recall: 0.6829,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.79, f_measure: 0.7197, kappa: 0.0484, kb_relative_information_score: 0.1342, mean_absolute_error: 0.2729, mean_prior_absolute_error: 0.3266, weighted_recall: 0.7931, number_of_instances: 522, precision: 0.7295, predictive_accuracy: 0.7931, prior_entropy: 0.7318, relative_absolute_error: 0.8357, root_mean_prior_squared_error: 0.4037, root_mean_squared_error: 0.3581, root_relative_squared_error: 0.887, unweighted_recall: 0.5161,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9196, f_measure: 0.861, kappa: 0.7189, kb_relative_information_score: 0.4436, mean_absolute_error: 0.3003, mean_prior_absolute_error: 0.494, weighted_recall: 0.8609, number_of_instances: 690, precision: 0.8612, predictive_accuracy: 0.8609, prior_entropy: 0.9912, relative_absolute_error: 0.6079, root_mean_prior_squared_error: 0.497, root_mean_squared_error: 0.3474, root_relative_squared_error: 0.6991, unweighted_recall: 0.8601,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.987, f_measure: 0.9933, kappa: 0.749, kb_relative_information_score: 0.0871, mean_absolute_error: 0.0099, mean_prior_absolute_error: 0.0292, weighted_recall: 0.9939, number_of_instances: 5100, precision: 0.9937, predictive_accuracy: 0.9939, prior_entropy: 0.1106, relative_absolute_error: 0.3383, root_mean_prior_squared_error: 0.1204, root_mean_squared_error: 0.0689, root_relative_squared_error: 0.5721, unweighted_recall: 0.813,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9544, f_measure: 0.9923, kappa: 0.7026, kb_relative_information_score: -37.6734, mean_absolute_error: 0.2983, mean_prior_absolute_error: 0.0292, weighted_recall: 0.9931, number_of_instances: 5100, precision: 0.9929, predictive_accuracy: 0.9931, prior_entropy: 0.1106, relative_absolute_error: 10.2299, root_mean_prior_squared_error: 0.1204, root_mean_squared_error: 0.301, root_relative_squared_error: 2.5005, unweighted_recall: 0.7798,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
5 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9895, f_measure: 0.994, kappa: 0.7791, kb_relative_information_score: 0.6528, mean_absolute_error: 0.0058, mean_prior_absolute_error: 0.0292, weighted_recall: 0.9943, number_of_instances: 5100, precision: 0.994, predictive_accuracy: 0.9943, prior_entropy: 0.1106, relative_absolute_error: 0.2006, root_mean_prior_squared_error: 0.1204, root_mean_squared_error: 0.0718, root_relative_squared_error: 0.5962, unweighted_recall: 0.8461,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9292, kb_relative_information_score: -0.5476, mean_absolute_error: 0.0246, mean_prior_absolute_error: 0.0292, weighted_recall: 0.9853, number_of_instances: 5100, predictive_accuracy: 0.9853, prior_entropy: 0.1106, relative_absolute_error: 0.8441, root_mean_prior_squared_error: 0.1204, root_mean_squared_error: 0.1053, root_relative_squared_error: 0.8749, unweighted_recall: 0.5,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
5 runs0 likes0 downloads0 reach0 impact
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
0 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
Imputation for completing missing values using k-Nearest Neighbors. Each sample's missing values are imputed using the mean value from `n_neighbors` nearest neighbors found in the training set. Two…
0 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
0 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
Multivariate imputer that estimates each feature from all the others. A strategy for imputing missing values by modeling each feature with missing values as a function of other features in a…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7976, f_measure: 0.7186, kappa: 0.4353, kb_relative_information_score: 0.2845, mean_absolute_error: 0.3704, mean_prior_absolute_error: 0.4984, weighted_recall: 0.7187, number_of_instances: 98050, precision: 0.7186, predictive_accuracy: 0.7187, prior_entropy: 0.9976, relative_absolute_error: 0.7432, root_mean_prior_squared_error: 0.4992, root_mean_squared_error: 0.4278, root_relative_squared_error: 0.8571, unweighted_recall: 0.7175,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8771, f_measure: 0.8605, kappa: 0.455, kb_relative_information_score: 0.3561, mean_absolute_error: 0.1522, mean_prior_absolute_error: 0.2752, weighted_recall: 0.8734, number_of_instances: 8378, precision: 0.8602, predictive_accuracy: 0.8734, prior_entropy: 0.6455, relative_absolute_error: 0.553, root_mean_prior_squared_error: 0.3709, root_mean_squared_error: 0.3094, root_relative_squared_error: 0.834, unweighted_recall: 0.6921,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9687, f_measure: 0.9994, kappa: 0.8086, kb_relative_information_score: -0.3086, mean_absolute_error: 0.0016, mean_prior_absolute_error: 0.0035, weighted_recall: 0.9994, number_of_instances: 284807, precision: 0.9993, predictive_accuracy: 0.9994, prior_entropy: 0.0183, relative_absolute_error: 0.4559, root_mean_prior_squared_error: 0.0415, root_mean_squared_error: 0.0238, root_relative_squared_error: 0.5741, unweighted_recall: 0.8871,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.955, f_measure: 0.9994, kappa: 0.8148, kb_relative_information_score: 0.1877, mean_absolute_error: 0.0008, mean_prior_absolute_error: 0.0035, weighted_recall: 0.9994, number_of_instances: 284807, precision: 0.9994, predictive_accuracy: 0.9994, prior_entropy: 0.0183, relative_absolute_error: 0.2322, root_mean_prior_squared_error: 0.0415, root_mean_squared_error: 0.0238, root_relative_squared_error: 0.5722, unweighted_recall: 0.8739,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9007, f_measure: 0.8872, kappa: 0.4136, kb_relative_information_score: 0.3037, mean_absolute_error: 0.1093, mean_prior_absolute_error: 0.2041, weighted_recall: 0.8945, number_of_instances: 4521, precision: 0.883, predictive_accuracy: 0.8945, prior_entropy: 0.5155, relative_absolute_error: 0.5355, root_mean_prior_squared_error: 0.3193, root_mean_squared_error: 0.2909, root_relative_squared_error: 0.9109, unweighted_recall: 0.6825,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8917, f_measure: 0.8896, kappa: 0.4251, kb_relative_information_score: 0.3129, mean_absolute_error: 0.1078, mean_prior_absolute_error: 0.2041, weighted_recall: 0.8971, number_of_instances: 4521, precision: 0.8857, predictive_accuracy: 0.8971, prior_entropy: 0.5155, relative_absolute_error: 0.5284, root_mean_prior_squared_error: 0.3193, root_mean_squared_error: 0.2929, root_relative_squared_error: 0.9171, unweighted_recall: 0.6865,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.898, f_measure: 0.8907, kappa: 0.4329, kb_relative_information_score: 0.3307, mean_absolute_error: 0.1053, mean_prior_absolute_error: 0.2041, weighted_recall: 0.8976, number_of_instances: 4521, precision: 0.8869, predictive_accuracy: 0.8976, prior_entropy: 0.5155, relative_absolute_error: 0.5159, root_mean_prior_squared_error: 0.3193, root_mean_squared_error: 0.2949, root_relative_squared_error: 0.9235, unweighted_recall: 0.6917,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8891, f_measure: 0.8873, kappa: 0.4147, kb_relative_information_score: 0.2671, mean_absolute_error: 0.1146, mean_prior_absolute_error: 0.2041, weighted_recall: 0.8945, number_of_instances: 4521, precision: 0.8832, predictive_accuracy: 0.8945, prior_entropy: 0.5155, relative_absolute_error: 0.5617, root_mean_prior_squared_error: 0.3193, root_mean_squared_error: 0.2857, root_relative_squared_error: 0.8948, unweighted_recall: 0.6833,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
11 runs0 likes0 downloads0 reach0 impact
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7341, f_measure: 0.8631, kappa: 0.2184, kb_relative_information_score: -0.0306, mean_absolute_error: 0.1797, mean_prior_absolute_error: 0.2041, weighted_recall: 0.8932, number_of_instances: 4521, precision: 0.8714, predictive_accuracy: 0.8932, prior_entropy: 0.5155, relative_absolute_error: 0.8806, root_mean_prior_squared_error: 0.3193, root_mean_squared_error: 0.2981, root_relative_squared_error: 0.9337, unweighted_recall: 0.5732,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8705, f_measure: 0.7995, kappa: 0.5954, kb_relative_information_score: 0.4069, mean_absolute_error: 0.3125, mean_prior_absolute_error: 0.4964, weighted_recall: 0.8001, number_of_instances: 3751, precision: 0.8, predictive_accuracy: 0.8001, prior_entropy: 0.9948, relative_absolute_error: 0.6296, root_mean_prior_squared_error: 0.4982, root_mean_squared_error: 0.3821, root_relative_squared_error: 0.7669, unweighted_recall: 0.7964,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8323, f_measure: 0.7642, kappa: 0.4744, kb_relative_information_score: 0.3421, mean_absolute_error: 0.3028, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7682, number_of_instances: 768, precision: 0.7634, predictive_accuracy: 0.7682, prior_entropy: 0.9331, relative_absolute_error: 0.6663, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3963, root_relative_squared_error: 0.8314, unweighted_recall: 0.7302,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8238, f_measure: 0.756, kappa: 0.4524, kb_relative_information_score: 0.2811, mean_absolute_error: 0.3339, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7643, number_of_instances: 768, precision: 0.7584, predictive_accuracy: 0.7643, prior_entropy: 0.9331, relative_absolute_error: 0.7347, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4013, root_relative_squared_error: 0.842, unweighted_recall: 0.7143,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8365, f_measure: 0.7582, kappa: 0.4579, kb_relative_information_score: 0.3006, mean_absolute_error: 0.3254, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7656, number_of_instances: 768, precision: 0.7598, predictive_accuracy: 0.7656, prior_entropy: 0.9331, relative_absolute_error: 0.716, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3949, root_relative_squared_error: 0.8284, unweighted_recall: 0.7179,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8394, f_measure: 0.7724, kappa: 0.4931, kb_relative_information_score: 0.3351, mean_absolute_error: 0.3074, mean_prior_absolute_error: 0.4545, weighted_recall: 0.776, number_of_instances: 768, precision: 0.7717, predictive_accuracy: 0.776, prior_entropy: 0.9331, relative_absolute_error: 0.6763, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3918, root_relative_squared_error: 0.8221, unweighted_recall: 0.7397,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9959, f_measure: 0.9959, kappa: 0.9918, kb_relative_information_score: 0.9918, mean_absolute_error: 0.0041, mean_prior_absolute_error: 0.499, weighted_recall: 0.9959, number_of_instances: 3196, precision: 0.9959, predictive_accuracy: 0.9959, prior_entropy: 0.9986, relative_absolute_error: 0.0082, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.0638, root_relative_squared_error: 0.1277, unweighted_recall: 0.9959,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9959, f_measure: 0.9959, kappa: 0.9918, kb_relative_information_score: 0.9918, mean_absolute_error: 0.0041, mean_prior_absolute_error: 0.499, weighted_recall: 0.9959, number_of_instances: 3196, precision: 0.9959, predictive_accuracy: 0.9959, prior_entropy: 0.9986, relative_absolute_error: 0.0082, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.0638, root_relative_squared_error: 0.1277, unweighted_recall: 0.9959,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9959, f_measure: 0.9959, kappa: 0.9918, kb_relative_information_score: 0.9918, mean_absolute_error: 0.0041, mean_prior_absolute_error: 0.499, weighted_recall: 0.9959, number_of_instances: 3196, precision: 0.9959, predictive_accuracy: 0.9959, prior_entropy: 0.9986, relative_absolute_error: 0.0082, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.0638, root_relative_squared_error: 0.1277, unweighted_recall: 0.9959,
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 36253 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : f_measure - target_feature : class
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 36253 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : area_under_roc_curve - target_feature : class
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9959, f_measure: 0.9959, kappa: 0.9918, kb_relative_information_score: 0.9918, mean_absolute_error: 0.0041, mean_prior_absolute_error: 0.499, weighted_recall: 0.9959, number_of_instances: 3196, precision: 0.9959, predictive_accuracy: 0.9959, prior_entropy: 0.9986, relative_absolute_error: 0.0082, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.0638, root_relative_squared_error: 0.1277, unweighted_recall: 0.9959,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
4 runs0 likes0 downloads0 reach0 impact
Encode categorical features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features.…
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Standardize features by removing the mean and scaling to unit variance. The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if…
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : medianHouseValue
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : SeriousDlqin2yrs
Binarized version of the California Housing Dataset This dataset was obtained from Luis Torgo's collection of regression datasets. It was binarized to serve as the original, unprocessed date for the…
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20640 instances - 9 features - 2 classes - 0 missing values
Improve on the state of the art in credit scoring by predicting the probability that somebody will experience financial distress in the next two years. yeah ## Description Banks play a crucial role in…
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150000 instances - 11 features - 2 classes - 33655 missing values
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Sex_of_Driver
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : two_year_recid
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class:
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : binaryClass
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : binaryClass
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Y
IoT Intrusion Dataset, can be used for device profiling, attack detection, and detection classification
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packet_priority_classification
1 datasets, 1 tasks, 0 flows, 0 runs
packet_priority_classification
1 datasets, 1 tasks, 0 flows, 0 runs
Data from the PASCAL Challenge 2008 as available on the LibSVM repository ## Description Preprocessing: The raw data set (epsilon_train) is instance-wisely scaled to unit length and split into two…
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500000 instances - 2001 features - 2 classes - 0 missing values
This dataset consists of 6,642 question/answer pairs. The questions are supposed to be answerable by Freebase, a large knowledge graph. The questions are mostly centered around a single named entity.…
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5810 instances - 3 features - classes - 0 missing values
HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the…
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97852 instances - 7 features - classes - 0 missing values
This is a classification problem to distinguish between a signal process which produces Higgs bosons and a background process which does not. ## Information The data has been produced using Monte…
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11000000 instances - 29 features - 2 classes - 0 missing values
DBLP-QuAD is a scholarly question answering dataset over the DBLP knowledge graph. The dataset can also be found at https://zenodo.org/record/7643971 and…
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10000 instances - 10 features - 9999 classes - 0 missing values
Hard tabular datasets from the TabZilla study.
36 datasets, 36 tasks, 0 flows, 0 runs
Teste
1 datasets, 1 tasks, 0 flows, 0 runs
Teste
1 datasets, 1 tasks, 0 flows, 0 runs
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9785, f_measure: 0.9613, kappa: 0.957, kb_relative_information_score: 0.9596, mean_absolute_error: 0.0077, mean_prior_absolute_error: 0.18, weighted_recall: 0.9613, number_of_instances: 10992, precision: 0.9614, predictive_accuracy: 0.9613, prior_entropy: 3.3208, relative_absolute_error: 0.043, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.0879, root_relative_squared_error: 0.2931, unweighted_recall: 0.9614,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5227, f_measure: 0.5486, kappa: 0.0507, kb_relative_information_score: 0.1682, mean_absolute_error: 0.3708, mean_prior_absolute_error: 0.456, weighted_recall: 0.6569, number_of_instances: 19020, precision: 0.6482, predictive_accuracy: 0.6569, prior_entropy: 0.9355, relative_absolute_error: 0.8133, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.547, root_relative_squared_error: 1.1455, unweighted_recall: 0.5201,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
Encode categorical features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features.…
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Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9359, f_measure: 0.8805, kappa: 0.7348, kb_relative_information_score: 0.5968, mean_absolute_error: 0.1946, mean_prior_absolute_error: 0.456, weighted_recall: 0.8824, number_of_instances: 19020, precision: 0.8821, predictive_accuracy: 0.8824, prior_entropy: 0.9355, relative_absolute_error: 0.4268, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2994, root_relative_squared_error: 0.6271, unweighted_recall: 0.8574,
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Churn
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : TARGET