1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37178 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
simple Math Function
0 runs0 likes0 downloads0 reach0 impact
1000 instances - 2 features - 0 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37163 - estimation_procedure : 10 times 10-fold Crossvalidation - target_feature : class
appendicities from edsa yang lain lain -- local computer
0 runs0 likes0 downloads0 reach0 impact
106 instances - 8 features - 2 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5778, f_measure: 0.7143, kappa: 0.3778, kb_relative_information_score: 0.0652, mean_absolute_error: 0.4343, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7143, number_of_instances: 14, precision: 0.7143, predictive_accuracy: 0.7143, prior_entropy: 0.9413, relative_absolute_error: 0.9354, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4991, root_relative_squared_error: 1.0409, unweighted_recall: 0.6889,
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6, f_measure: 0.7143, kappa: 0.3778, kb_relative_information_score: 0.0394, mean_absolute_error: 0.4429, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7143, number_of_instances: 14, precision: 0.7143, predictive_accuracy: 0.7143, prior_entropy: 0.9413, relative_absolute_error: 0.9538, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.5005, root_relative_squared_error: 1.0439, unweighted_recall: 0.6889,
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37163 - estimation_procedure : 10 times 10-fold Crossvalidation - target_feature : class
2 runs0 likes0 downloads0 reach0 impact
uploader_id : 37177 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
appendicities from edsa yang lain lain -- local computer
0 runs0 likes0 downloads0 reach0 impact
106 instances - 8 features - 2 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6556, f_measure: 0.7794, kappa: 0.5116, kb_relative_information_score: 0.0834, mean_absolute_error: 0.4243, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7857, number_of_instances: 14, precision: 0.7821, predictive_accuracy: 0.7857, prior_entropy: 0.9413, relative_absolute_error: 0.9138, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4846, root_relative_squared_error: 1.0107, unweighted_recall: 0.7444,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6222, f_measure: 0.7143, kappa: 0.3778, kb_relative_information_score: 0.0594, mean_absolute_error: 0.4379, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7143, number_of_instances: 14, precision: 0.7143, predictive_accuracy: 0.7143, prior_entropy: 0.9413, relative_absolute_error: 0.9431, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4975, root_relative_squared_error: 1.0375, unweighted_recall: 0.6889,
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6444, f_measure: 0.6929, kappa: 0.3171, kb_relative_information_score: 0.1091, mean_absolute_error: 0.4186, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7143, number_of_instances: 14, precision: 0.7056, predictive_accuracy: 0.7143, prior_entropy: 0.9413, relative_absolute_error: 0.9015, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.478, root_relative_squared_error: 0.9968, unweighted_recall: 0.6444,
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37184 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6444, f_measure: 0.7794, kappa: 0.5116, kb_relative_information_score: 0.1083, mean_absolute_error: 0.4214, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7857, number_of_instances: 14, precision: 0.7821, predictive_accuracy: 0.7857, prior_entropy: 0.9413, relative_absolute_error: 0.9077, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4862, root_relative_squared_error: 1.014, unweighted_recall: 0.7444,
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37183 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37164 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6222, f_measure: 0.7794, kappa: 0.5116, kb_relative_information_score: 0.0754, mean_absolute_error: 0.4314, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7857, number_of_instances: 14, precision: 0.7821, predictive_accuracy: 0.7857, prior_entropy: 0.9413, relative_absolute_error: 0.9292, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4937, root_relative_squared_error: 1.0297, unweighted_recall: 0.7444,
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37159 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37185 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6, f_measure: 0.7143, kappa: 0.3778, kb_relative_information_score: 0.0604, mean_absolute_error: 0.4336, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7143, number_of_instances: 14, precision: 0.7143, predictive_accuracy: 0.7143, prior_entropy: 0.9413, relative_absolute_error: 0.9338, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.504, root_relative_squared_error: 1.0512, unweighted_recall: 0.6889,
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…
9 runs0 likes0 downloads0 reach8 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
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…
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
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…
4 runs0 likes0 downloads0 reach4 impact
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37190 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6222, f_measure: 0.7794, kappa: 0.5116, kb_relative_information_score: 0.0849, mean_absolute_error: 0.4293, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7857, number_of_instances: 14, precision: 0.7821, predictive_accuracy: 0.7857, prior_entropy: 0.9413, relative_absolute_error: 0.9246, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4916, root_relative_squared_error: 1.0252, unweighted_recall: 0.7444,
randomly create description
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1000 instances - 7 features - 2 classes - 0 missing values
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37152 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
tbd
0 runs0 likes0 downloads0 reach0 impact
80000 instances - 20 features - classes - 0 missing values
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37171 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
tbd
0 runs0 likes0 downloads0 reach0 impact
56 instances - 13 features - 0 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37163 - estimation_procedure : 10 times 10-fold Crossvalidation - target_feature : class
appendicities from edsa yang lain -- local computer
0 runs0 likes0 downloads0 reach0 impact
106 instances - 8 features - 0 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
appendicities from edsa - local computer
0 runs0 likes0 downloads0 reach0 impact
106 instances - 8 features - 0 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6444, f_measure: 0.7794, kappa: 0.5116, kb_relative_information_score: 0.1014, mean_absolute_error: 0.4186, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7857, number_of_instances: 14, precision: 0.7821, predictive_accuracy: 0.7857, prior_entropy: 0.9413, relative_absolute_error: 0.9015, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4894, root_relative_squared_error: 1.0206, unweighted_recall: 0.7444,
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37163 - estimation_procedure : 10 times 10-fold Crossvalidation - target_feature : target
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6222, f_measure: 0.6929, kappa: 0.3171, kb_relative_information_score: 0.1267, mean_absolute_error: 0.4129, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7143, number_of_instances: 14, precision: 0.7056, predictive_accuracy: 0.7143, prior_entropy: 0.9413, relative_absolute_error: 0.8892, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4827, root_relative_squared_error: 1.0067, unweighted_recall: 0.6444,
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
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…
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
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
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…
0 runs0 likes0 downloads0 reach0 impact
appendicities from edsa - local computer
0 runs0 likes0 downloads0 reach0 impact
106 instances - 8 features - 0 classes - 0 missing values
2 runs0 likes0 downloads0 reach0 impact
uploader_id : 37152 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
tbd
0 runs0 likes0 downloads0 reach0 impact
10000 instances - 14 features - 0 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6222, f_measure: 0.6929, kappa: 0.3171, kb_relative_information_score: 0.1084, mean_absolute_error: 0.42, mean_prior_absolute_error: 0.4643, weighted_recall: 0.7143, number_of_instances: 14, precision: 0.7056, predictive_accuracy: 0.7143, prior_entropy: 0.9413, relative_absolute_error: 0.9046, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4833, root_relative_squared_error: 1.008, unweighted_recall: 0.6444,
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37163 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : play
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6, f_measure: 0.6324, kappa: 0.186, kb_relative_information_score: 0.0569, mean_absolute_error: 0.4343, mean_prior_absolute_error: 0.4643, weighted_recall: 0.6429, number_of_instances: 14, precision: 0.6286, predictive_accuracy: 0.6429, prior_entropy: 0.9413, relative_absolute_error: 0.9354, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4977, root_relative_squared_error: 1.038, unweighted_recall: 0.5889,
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…
6 runs0 likes0 downloads0 reach4 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
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…
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
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
2 runs0 likes0 downloads0 reach0 impact
uploader_id : 37163 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9371, f_measure: 0.8779, kappa: 0.7289, kb_relative_information_score: 0.5976, mean_absolute_error: 0.1942, mean_prior_absolute_error: 0.456, weighted_recall: 0.8798, number_of_instances: 19020, precision: 0.8795, predictive_accuracy: 0.8798, prior_entropy: 0.9355, relative_absolute_error: 0.4259, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2989, root_relative_squared_error: 0.626, unweighted_recall: 0.8544,
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…
14 runs0 likes0 downloads0 reach13 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9793, f_measure: 0.9628, kappa: 0.9586, kb_relative_information_score: 0.9611, mean_absolute_error: 0.0074, mean_prior_absolute_error: 0.18, weighted_recall: 0.9628, number_of_instances: 10992, precision: 0.9628, predictive_accuracy: 0.9628, prior_entropy: 3.3208, relative_absolute_error: 0.0414, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.0863, root_relative_squared_error: 0.2876, unweighted_recall: 0.9628,
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 reach3 impact
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…
0 runs0 likes0 downloads0 reach0 impact
A decision tree classifier.
0 runs0 likes0 downloads0 reach0 impact
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6384, kappa: 0.1024, kb_relative_information_score: 0.1397, mean_absolute_error: 0.172, mean_prior_absolute_error: 0.1799, weighted_recall: 0.1986, number_of_instances: 70000, predictive_accuracy: 0.1986, prior_entropy: 3.3198, relative_absolute_error: 0.9558, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2933, root_relative_squared_error: 0.9777, unweighted_recall: 0.1833,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5219, kappa: 0.0289, kb_relative_information_score: 0.0432, mean_absolute_error: 0.2757, mean_prior_absolute_error: 0.2774, weighted_recall: 0.1882, number_of_instances: 797, predictive_accuracy: 0.1882, prior_entropy: 2.5803, relative_absolute_error: 0.994, root_mean_prior_squared_error: 0.3724, root_mean_squared_error: 0.3717, root_relative_squared_error: 0.9981, unweighted_recall: 0.1924,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.773, kappa: 0.4341, kb_relative_information_score: 0.3574, mean_absolute_error: 0.2447, mean_prior_absolute_error: 0.3439, weighted_recall: 0.6421, number_of_instances: 841, predictive_accuracy: 0.6421, prior_entropy: 1.7874, relative_absolute_error: 0.7116, root_mean_prior_squared_error: 0.4146, root_mean_squared_error: 0.355, root_relative_squared_error: 0.8562, unweighted_recall: 0.44,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6231, kappa: 0.0397, kb_relative_information_score: 0.1224, mean_absolute_error: 0.0713, mean_prior_absolute_error: 0.074, weighted_recall: 0.0767, number_of_instances: 7797, predictive_accuracy: 0.0767, prior_entropy: 4.7004, relative_absolute_error: 0.9637, root_mean_prior_squared_error: 0.1923, root_mean_squared_error: 0.1888, root_relative_squared_error: 0.9817, unweighted_recall: 0.0767,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4849, kappa: -0.0844, kb_relative_information_score: 0.0305, mean_absolute_error: 0.1668, mean_prior_absolute_error: 0.1653, weighted_recall: 0.0141, number_of_instances: 990, predictive_accuracy: 0.0141, prior_entropy: 3.4594, relative_absolute_error: 1.0094, root_mean_prior_squared_error: 0.2875, root_mean_squared_error: 0.2992, root_relative_squared_error: 1.0408, unweighted_recall: 0.0141,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9141, f_measure: 0.9679, kappa: 0.742, kb_relative_information_score: 0.4961, mean_absolute_error: 0.0502, mean_prior_absolute_error: 0.1152, weighted_recall: 0.9655, number_of_instances: 3772, precision: 0.9725, predictive_accuracy: 0.9655, prior_entropy: 0.3324, relative_absolute_error: 0.4355, root_mean_prior_squared_error: 0.2398, root_mean_squared_error: 0.1587, root_relative_squared_error: 0.6617, unweighted_recall: 0.9311,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6867, kappa: 0.2488, kb_relative_information_score: 0.2595, mean_absolute_error: 0.2709, mean_prior_absolute_error: 0.3132, weighted_recall: 0.4457, number_of_instances: 736, predictive_accuracy: 0.4457, prior_entropy: 2.2621, relative_absolute_error: 0.8649, root_mean_prior_squared_error: 0.3957, root_mean_squared_error: 0.3682, root_relative_squared_error: 0.9304, unweighted_recall: 0.338,
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,