0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6879, kappa: 0.2525, kb_relative_information_score: 0.2491, mean_absolute_error: 0.2207, mean_prior_absolute_error: 0.2701, weighted_recall: 0.4257, number_of_instances: 6430, predictive_accuracy: 0.4257, prior_entropy: 2.4834, relative_absolute_error: 0.8171, root_mean_prior_squared_error: 0.3675, root_mean_squared_error: 0.3327, root_relative_squared_error: 0.9052, unweighted_recall: 0.3153,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7283, f_measure: 0.7486, kappa: 0.484, kb_relative_information_score: 0.2912, mean_absolute_error: 0.3659, mean_prior_absolute_error: 0.4886, weighted_recall: 0.7574, number_of_instances: 45312, precision: 0.7651, predictive_accuracy: 0.7574, prior_entropy: 0.9835, relative_absolute_error: 0.7488, root_mean_prior_squared_error: 0.4943, root_mean_squared_error: 0.4277, root_relative_squared_error: 0.8653, unweighted_recall: 0.7328,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6416, kappa: 0.1935, kb_relative_information_score: 0.1955, mean_absolute_error: 0.3386, mean_prior_absolute_error: 0.3748, weighted_recall: 0.3901, number_of_instances: 846, predictive_accuracy: 0.3901, prior_entropy: 1.9991, relative_absolute_error: 0.9033, root_mean_prior_squared_error: 0.4329, root_mean_squared_error: 0.4124, root_relative_squared_error: 0.9526, unweighted_recall: 0.4022,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6491, f_measure: 0.7002, kappa: 0.34, kb_relative_information_score: 0.1265, mean_absolute_error: 0.4008, mean_prior_absolute_error: 0.453, weighted_recall: 0.6994, number_of_instances: 958, precision: 0.7011, predictive_accuracy: 0.6994, prior_entropy: 0.931, relative_absolute_error: 0.8847, root_mean_prior_squared_error: 0.4759, root_mean_squared_error: 0.4479, root_relative_squared_error: 0.9411, unweighted_recall: 0.6709,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7631, kappa: 0.4122, kb_relative_information_score: 0.334, mean_absolute_error: 0.3013, mean_prior_absolute_error: 0.4101, weighted_recall: 0.6238, number_of_instances: 3190, predictive_accuracy: 0.6238, prior_entropy: 1.4802, relative_absolute_error: 0.7348, root_mean_prior_squared_error: 0.4528, root_mean_squared_error: 0.3882, root_relative_squared_error: 0.8574, unweighted_recall: 0.579,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7762, f_measure: 0.7754, kappa: 0.5248, kb_relative_information_score: 0.3459, mean_absolute_error: 0.3297, mean_prior_absolute_error: 0.4776, weighted_recall: 0.7798, number_of_instances: 4601, precision: 0.7784, predictive_accuracy: 0.7798, prior_entropy: 0.9674, relative_absolute_error: 0.6904, root_mean_prior_squared_error: 0.4886, root_mean_squared_error: 0.4116, root_relative_squared_error: 0.8424, unweighted_recall: 0.7546,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6785, f_measure: 0.7133, kappa: 0.3621, kb_relative_information_score: 0.1738, mean_absolute_error: 0.3832, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7174, number_of_instances: 768, precision: 0.7114, predictive_accuracy: 0.7174, prior_entropy: 0.9331, relative_absolute_error: 0.843, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.446, root_relative_squared_error: 0.9357, unweighted_recall: 0.6765,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6747, kappa: 0.1105, kb_relative_information_score: 0.1942, mean_absolute_error: 0.1658, mean_prior_absolute_error: 0.18, weighted_recall: 0.2031, number_of_instances: 10992, predictive_accuracy: 0.2031, prior_entropy: 3.3208, relative_absolute_error: 0.9215, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.288, root_relative_squared_error: 0.9602, unweighted_recall: 0.1952,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6506, kb_relative_information_score: 0.061, mean_absolute_error: 0.3766, mean_prior_absolute_error: 0.4202, weighted_recall: 0.7, number_of_instances: 1000, predictive_accuracy: 0.7, prior_entropy: 0.8813, relative_absolute_error: 0.8963, root_mean_prior_squared_error: 0.4583, root_mean_squared_error: 0.4341, root_relative_squared_error: 0.9472, unweighted_recall: 0.5,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8407, f_measure: 0.8554, kappa: 0.7116, kb_relative_information_score: 0.5565, mean_absolute_error: 0.2379, mean_prior_absolute_error: 0.494, weighted_recall: 0.8551, number_of_instances: 690, precision: 0.8663, predictive_accuracy: 0.8551, prior_entropy: 0.9912, relative_absolute_error: 0.4815, root_mean_prior_squared_error: 0.497, root_mean_squared_error: 0.3452, root_relative_squared_error: 0.6946, unweighted_recall: 0.862,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5909, kappa: 0.104, kb_relative_information_score: 0.1232, mean_absolute_error: 0.1679, mean_prior_absolute_error: 0.18, weighted_recall: 0.1947, number_of_instances: 5620, predictive_accuracy: 0.1947, prior_entropy: 3.3218, relative_absolute_error: 0.9327, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2898, root_relative_squared_error: 0.9658, unweighted_recall: 0.1944,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5495, kb_relative_information_score: 0.0624, mean_absolute_error: 0.4091, mean_prior_absolute_error: 0.4308, weighted_recall: 0.427, number_of_instances: 1473, predictive_accuracy: 0.427, prior_entropy: 1.539, relative_absolute_error: 0.9496, root_mean_prior_squared_error: 0.4641, root_mean_squared_error: 0.4523, root_relative_squared_error: 0.9745, unweighted_recall: 0.3333,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5807, kappa: 0.095, kb_relative_information_score: 0.1171, mean_absolute_error: 0.1645, mean_prior_absolute_error: 0.18, weighted_recall: 0.1855, number_of_instances: 2000, predictive_accuracy: 0.1855, prior_entropy: 3.3219, relative_absolute_error: 0.914, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2871, root_relative_squared_error: 0.9568, unweighted_recall: 0.1855,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.758, kappa: 0.1106, kb_relative_information_score: 0.2861, mean_absolute_error: 0.1606, mean_prior_absolute_error: 0.18, weighted_recall: 0.1995, number_of_instances: 2000, predictive_accuracy: 0.1995, prior_entropy: 3.3219, relative_absolute_error: 0.8923, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2834, root_relative_squared_error: 0.9446, unweighted_recall: 0.1995,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6396, kappa: 0.0972, kb_relative_information_score: 0.1665, mean_absolute_error: 0.1671, mean_prior_absolute_error: 0.18, weighted_recall: 0.1875, number_of_instances: 2000, predictive_accuracy: 0.1875, prior_entropy: 3.3219, relative_absolute_error: 0.9285, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2892, root_relative_squared_error: 0.964, unweighted_recall: 0.1875,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9246, f_measure: 0.9143, kappa: 0.8108, kb_relative_information_score: 0.7085, mean_absolute_error: 0.1431, mean_prior_absolute_error: 0.4519, weighted_recall: 0.9142, number_of_instances: 699, precision: 0.9146, predictive_accuracy: 0.9142, prior_entropy: 0.9293, relative_absolute_error: 0.3166, root_mean_prior_squared_error: 0.4753, root_mean_squared_error: 0.2756, root_relative_squared_error: 0.5798, unweighted_recall: 0.907,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5831, kappa: 0.1011, kb_relative_information_score: 0.1224, mean_absolute_error: 0.1642, mean_prior_absolute_error: 0.18, weighted_recall: 0.191, number_of_instances: 2000, predictive_accuracy: 0.191, prior_entropy: 3.3219, relative_absolute_error: 0.9125, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2867, root_relative_squared_error: 0.9558, unweighted_recall: 0.191,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5848, kappa: 0.1, kb_relative_information_score: 0.1215, mean_absolute_error: 0.1644, mean_prior_absolute_error: 0.18, weighted_recall: 0.19, number_of_instances: 2000, predictive_accuracy: 0.19, prior_entropy: 3.3219, relative_absolute_error: 0.9133, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2868, root_relative_squared_error: 0.9559, unweighted_recall: 0.19,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6114, kappa: 0.2285, kb_relative_information_score: 0.1077, mean_absolute_error: 0.349, mean_prior_absolute_error: 0.3798, weighted_recall: 0.584, number_of_instances: 625, predictive_accuracy: 0.584, prior_entropy: 1.3181, relative_absolute_error: 0.919, root_mean_prior_squared_error: 0.4356, root_mean_squared_error: 0.4259, root_relative_squared_error: 0.9778, unweighted_recall: 0.4225,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5495, kappa: 0.0326, kb_relative_information_score: 0.0575, mean_absolute_error: 0.0723, mean_prior_absolute_error: 0.074, weighted_recall: 0.0718, number_of_instances: 20000, predictive_accuracy: 0.0718, prior_entropy: 4.6998, relative_absolute_error: 0.9776, root_mean_prior_squared_error: 0.1923, root_mean_squared_error: 0.1901, root_relative_squared_error: 0.9887, unweighted_recall: 0.0688,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6664, f_measure: 0.6233, kappa: 0.3396, kb_relative_information_score: 0.2118, mean_absolute_error: 0.397, mean_prior_absolute_error: 0.499, weighted_recall: 0.6605, number_of_instances: 3196, precision: 0.8015, predictive_accuracy: 0.6605, prior_entropy: 0.9986, relative_absolute_error: 0.7955, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.4455, root_relative_squared_error: 0.8919, unweighted_recall: 0.675,
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…
112 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
A decision tree classifier.
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9371, f_measure: 0.8801, kappa: 0.734, kb_relative_information_score: 0.5987, mean_absolute_error: 0.1938, mean_prior_absolute_error: 0.456, weighted_recall: 0.8821, number_of_instances: 19020, precision: 0.8818, predictive_accuracy: 0.8821, prior_entropy: 0.9355, relative_absolute_error: 0.425, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2983, root_relative_squared_error: 0.6247, unweighted_recall: 0.8568,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9369, f_measure: 0.8802, kappa: 0.7341, kb_relative_information_score: 0.5984, mean_absolute_error: 0.1938, mean_prior_absolute_error: 0.456, weighted_recall: 0.8821, number_of_instances: 19020, precision: 0.8819, predictive_accuracy: 0.8821, prior_entropy: 0.9355, relative_absolute_error: 0.425, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2987, root_relative_squared_error: 0.6255, unweighted_recall: 0.8568,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9365, f_measure: 0.8811, kappa: 0.7361, kb_relative_information_score: 0.5978, mean_absolute_error: 0.1941, mean_prior_absolute_error: 0.456, weighted_recall: 0.883, number_of_instances: 19020, precision: 0.8827, predictive_accuracy: 0.883, prior_entropy: 0.9355, relative_absolute_error: 0.4256, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2989, root_relative_squared_error: 0.6259, unweighted_recall: 0.858,
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
1 runs0 likes0 downloads0 reach0 impact
Pre-cleaned version of Edge-IIoTset
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1909671 instances - 49 features - classes - 0 missing values
Pre-cleaned version of Edge-IIoTset
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1909671 instances - 49 features - classes - 0 missing values
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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 : Target
Anonymized dataset of churn and uplift modeling from a series of marketing campaigns in 2020 by a telecom company.
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11896 instances - 180 features - 0 classes - 0 missing values
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : relevance
Microsoft Learning to Rank Datasets ## Dataset Descriptions The datasets are machine learning data, in which queries and urls are represented by IDs. The datasets consist of feature vectors extracted…
0 runs0 likes0 downloads0 reach0 impact
1200192 instances - 137 features - 5 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9798, f_measure: 0.9636, kappa: 0.9596, kb_relative_information_score: 0.9619, mean_absolute_error: 0.0073, mean_prior_absolute_error: 0.18, weighted_recall: 0.9636, number_of_instances: 10992, precision: 0.9636, predictive_accuracy: 0.9636, prior_entropy: 3.3208, relative_absolute_error: 0.0404, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.0853, root_relative_squared_error: 0.2844, unweighted_recall: 0.9636,
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
A decision tree classifier.
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.937, f_measure: 0.8789, kappa: 0.7312, kb_relative_information_score: 0.5975, mean_absolute_error: 0.1941, mean_prior_absolute_error: 0.456, weighted_recall: 0.881, number_of_instances: 19020, precision: 0.8808, predictive_accuracy: 0.881, prior_entropy: 0.9355, relative_absolute_error: 0.4256, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2991, root_relative_squared_error: 0.6263, unweighted_recall: 0.855,
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
3 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9996, f_measure: 0.9928, kappa: 0.9856, kb_relative_information_score: 0.9801, mean_absolute_error: 0.0108, mean_prior_absolute_error: 0.499, weighted_recall: 0.9928, number_of_instances: 3196, precision: 0.9928, predictive_accuracy: 0.9928, prior_entropy: 0.9986, relative_absolute_error: 0.0217, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.0748, root_relative_squared_error: 0.1497, unweighted_recall: 0.9928,
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
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: 0.9996, f_measure: 0.9925, kappa: 0.985, kb_relative_information_score: 0.9674, mean_absolute_error: 0.019, mean_prior_absolute_error: 0.499, weighted_recall: 0.9925, number_of_instances: 3196, precision: 0.9925, predictive_accuracy: 0.9925, prior_entropy: 0.9986, relative_absolute_error: 0.0382, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.078, root_relative_squared_error: 0.1562, unweighted_recall: 0.9925,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9996, f_measure: 0.9919, kappa: 0.9837, kb_relative_information_score: 0.9648, mean_absolute_error: 0.0204, mean_prior_absolute_error: 0.499, weighted_recall: 0.9919, number_of_instances: 3196, precision: 0.9919, predictive_accuracy: 0.9919, prior_entropy: 0.9986, relative_absolute_error: 0.0408, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.083, root_relative_squared_error: 0.1662, unweighted_recall: 0.9918,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9977, f_measure: 0.9759, kappa: 0.9517, kb_relative_information_score: 0.8999, mean_absolute_error: 0.0573, mean_prior_absolute_error: 0.499, weighted_recall: 0.9759, number_of_instances: 3196, precision: 0.9759, predictive_accuracy: 0.9759, prior_entropy: 0.9986, relative_absolute_error: 0.1147, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.1365, root_relative_squared_error: 0.2732, unweighted_recall: 0.9757,
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…
3 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
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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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
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.…
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
Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero…
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…
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Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier` for big datasets (n_samples…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9998, f_measure: 0.9947, kappa: 0.9893, kb_relative_information_score: 0.9871, mean_absolute_error: 0.0067, mean_prior_absolute_error: 0.499, weighted_recall: 0.9947, number_of_instances: 3196, precision: 0.9947, predictive_accuracy: 0.9947, prior_entropy: 0.9986, relative_absolute_error: 0.0134, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.0675, root_relative_squared_error: 0.1352, unweighted_recall: 0.9947,
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
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
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.…
0 runs0 likes0 downloads0 reach0 impact
Imputation transformer for completing missing values.
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
Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero…
0 runs0 likes0 downloads0 reach0 impact
Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier` for big datasets (n_samples…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9995, f_measure: 0.9944, kappa: 0.9887, kb_relative_information_score: 0.9872, mean_absolute_error: 0.0067, mean_prior_absolute_error: 0.499, weighted_recall: 0.9944, number_of_instances: 3196, precision: 0.9944, predictive_accuracy: 0.9944, prior_entropy: 0.9986, relative_absolute_error: 0.0134, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.0678, root_relative_squared_error: 0.1357, unweighted_recall: 0.9944,
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
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
Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero…
0 runs0 likes0 downloads0 reach0 impact
Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier` for big datasets (n_samples…
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9889, f_measure: 0.9386, kappa: 0.8768, kb_relative_information_score: 0.8335, mean_absolute_error: 0.0908, mean_prior_absolute_error: 0.499, weighted_recall: 0.9387, number_of_instances: 3196, precision: 0.9397, predictive_accuracy: 0.9387, prior_entropy: 0.9986, relative_absolute_error: 0.1819, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.218, root_relative_squared_error: 0.4364, unweighted_recall: 0.9374,
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
C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
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…
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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…
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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…
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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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Univariate imputer for completing missing values with simple strategies. Replace missing values using a descriptive statistic (e.g. mean, median, or most frequent) along each column, or using a…
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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…
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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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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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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…
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Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier` for big datasets (n_samples…
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5356, f_measure: 0.0631, kappa: -0.0256, kb_relative_information_score: 0.096, mean_absolute_error: 0.1648, mean_prior_absolute_error: 0.1653, weighted_recall: 0.0677, number_of_instances: 990, precision: 0.0634, predictive_accuracy: 0.0677, prior_entropy: 3.4594, relative_absolute_error: 0.9972, root_mean_prior_squared_error: 0.2875, root_mean_squared_error: 0.3131, root_relative_squared_error: 1.0892, unweighted_recall: 0.0677,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9475, f_measure: 0.9675, kappa: 0.7368, kb_relative_information_score: 0.4355, mean_absolute_error: 0.0618, mean_prior_absolute_error: 0.1152, weighted_recall: 0.9653, number_of_instances: 3772, precision: 0.9716, predictive_accuracy: 0.9653, prior_entropy: 0.3324, relative_absolute_error: 0.5365, root_mean_prior_squared_error: 0.2398, root_mean_squared_error: 0.1613, root_relative_squared_error: 0.6725, unweighted_recall: 0.9228,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8766, f_measure: 0.5353, kappa: 0.4489, kb_relative_information_score: 0.3578, mean_absolute_error: 0.2432, mean_prior_absolute_error: 0.3132, weighted_recall: 0.587, number_of_instances: 736, precision: 0.588, predictive_accuracy: 0.587, prior_entropy: 2.2621, relative_absolute_error: 0.7764, root_mean_prior_squared_error: 0.3957, root_mean_squared_error: 0.3314, root_relative_squared_error: 0.8375, unweighted_recall: 0.4947,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.976, f_measure: 0.8033, kappa: 0.7861, kb_relative_information_score: 0.6911, mean_absolute_error: 0.1137, mean_prior_absolute_error: 0.2701, weighted_recall: 0.8299, number_of_instances: 6430, precision: 0.8206, predictive_accuracy: 0.8299, prior_entropy: 2.4834, relative_absolute_error: 0.4209, root_mean_prior_squared_error: 0.3675, root_mean_squared_error: 0.2067, root_relative_squared_error: 0.5624, unweighted_recall: 0.7529,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8275, f_measure: 0.753, kappa: 0.4937, kb_relative_information_score: 0.2492, mean_absolute_error: 0.3863, mean_prior_absolute_error: 0.4886, weighted_recall: 0.7628, number_of_instances: 45312, precision: 0.7738, predictive_accuracy: 0.7628, prior_entropy: 0.9835, relative_absolute_error: 0.7905, root_mean_prior_squared_error: 0.4943, root_mean_squared_error: 0.419, root_relative_squared_error: 0.8477, unweighted_recall: 0.7367,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9202, f_measure: 0.6852, kappa: 0.6094, kb_relative_information_score: 0.5779, mean_absolute_error: 0.1974, mean_prior_absolute_error: 0.3748, weighted_recall: 0.7069, number_of_instances: 846, precision: 0.6833, predictive_accuracy: 0.7069, prior_entropy: 1.9991, relative_absolute_error: 0.5267, root_mean_prior_squared_error: 0.4329, root_mean_squared_error: 0.2952, root_relative_squared_error: 0.6819, unweighted_recall: 0.7105,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8492, f_measure: 0.7469, kappa: 0.4276, kb_relative_information_score: 0.3161, mean_absolute_error: 0.3123, mean_prior_absolute_error: 0.453, weighted_recall: 0.7683, number_of_instances: 958, precision: 0.7748, predictive_accuracy: 0.7683, prior_entropy: 0.931, relative_absolute_error: 0.6894, root_mean_prior_squared_error: 0.4759, root_mean_squared_error: 0.371, root_relative_squared_error: 0.7796, unweighted_recall: 0.6911,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9675, f_measure: 0.8701, kappa: 0.7888, kb_relative_information_score: 0.4838, mean_absolute_error: 0.2585, mean_prior_absolute_error: 0.4101, weighted_recall: 0.8702, number_of_instances: 3190, precision: 0.8701, predictive_accuracy: 0.8702, prior_entropy: 1.4802, relative_absolute_error: 0.6304, root_mean_prior_squared_error: 0.4528, root_mean_squared_error: 0.3125, root_relative_squared_error: 0.6901, unweighted_recall: 0.8537,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9514, f_measure: 0.8993, kappa: 0.7872, kb_relative_information_score: 0.4609, mean_absolute_error: 0.2856, mean_prior_absolute_error: 0.4776, weighted_recall: 0.9007, number_of_instances: 4601, precision: 0.9029, predictive_accuracy: 0.9007, prior_entropy: 0.9674, relative_absolute_error: 0.598, root_mean_prior_squared_error: 0.4886, root_mean_squared_error: 0.326, root_relative_squared_error: 0.6672, unweighted_recall: 0.8848,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8209, f_measure: 0.7413, kappa: 0.419, kb_relative_information_score: 0.2425, mean_absolute_error: 0.3536, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7513, number_of_instances: 768, precision: 0.7442, predictive_accuracy: 0.7513, prior_entropy: 0.9331, relative_absolute_error: 0.778, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4066, root_relative_squared_error: 0.853, unweighted_recall: 0.6973,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9877, f_measure: 0.8478, kappa: 0.8355, kb_relative_information_score: 0.6503, mean_absolute_error: 0.1027, mean_prior_absolute_error: 0.18, weighted_recall: 0.852, number_of_instances: 10992, precision: 0.8601, predictive_accuracy: 0.852, prior_entropy: 3.3208, relative_absolute_error: 0.5708, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.1905, root_relative_squared_error: 0.6352, unweighted_recall: 0.8501,