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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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0 likes - 0 downloads - 0 reach - No evaluations yet (or not applicable).
Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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uploader_id : 86 - estimation_procedure : 10-fold Crossvalidation - target_feature : Label
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 1, f_measure: 1, kappa: 1, kb_relative_information_score: 0.9794, mean_absolute_error: 0.0118, mean_prior_absolute_error: 0.5, weighted_recall: 1, number_of_instances: 128, precision: 1, predictive_accuracy: 1, prior_entropy: 1, relative_absolute_error: 0.0236, root_mean_prior_squared_error: 0.5, root_mean_squared_error: 0.0605, root_relative_squared_error: 0.1211, unweighted_recall: 1,
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
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Transform the data so that it can be processed by PDL models.
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Works on binary and Multi class classification
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8331, f_measure: 0.776, kappa: 0.5006, kb_relative_information_score: 0.3226, mean_absolute_error: 0.3142, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7799, number_of_instances: 768, precision: 0.7755, predictive_accuracy: 0.7799, prior_entropy: 0.9331, relative_absolute_error: 0.6913, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3955, root_relative_squared_error: 0.8298, unweighted_recall: 0.7427,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8278, f_measure: 0.7687, kappa: 0.4835, kb_relative_information_score: 0.3134, mean_absolute_error: 0.3175, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7734, number_of_instances: 768, precision: 0.7685, predictive_accuracy: 0.7734, prior_entropy: 0.9331, relative_absolute_error: 0.6986, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3988, root_relative_squared_error: 0.8366, unweighted_recall: 0.7334,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8216, f_measure: 0.7604, kappa: 0.4647, kb_relative_information_score: 0.3092, mean_absolute_error: 0.3196, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7656, number_of_instances: 768, precision: 0.7601, predictive_accuracy: 0.7656, prior_entropy: 0.9331, relative_absolute_error: 0.7032, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4016, root_relative_squared_error: 0.8425, unweighted_recall: 0.7239,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8273, f_measure: 0.7751, kappa: 0.499, kb_relative_information_score: 0.3138, mean_absolute_error: 0.3175, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7786, number_of_instances: 768, precision: 0.7744, predictive_accuracy: 0.7786, prior_entropy: 0.9331, relative_absolute_error: 0.6986, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3993, root_relative_squared_error: 0.8376, unweighted_recall: 0.7426,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8211, f_measure: 0.7482, kappa: 0.4374, kb_relative_information_score: 0.3092, mean_absolute_error: 0.3184, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7539, number_of_instances: 768, precision: 0.7477, predictive_accuracy: 0.7539, prior_entropy: 0.9331, relative_absolute_error: 0.7005, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4022, root_relative_squared_error: 0.8438, unweighted_recall: 0.7106,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8225, f_measure: 0.7606, kappa: 0.467, kb_relative_information_score: 0.3119, mean_absolute_error: 0.3179, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7643, number_of_instances: 768, precision: 0.7596, predictive_accuracy: 0.7643, prior_entropy: 0.9331, relative_absolute_error: 0.6994, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4017, root_relative_squared_error: 0.8427, unweighted_recall: 0.7272,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8232, f_measure: 0.7482, kappa: 0.4374, kb_relative_information_score: 0.3084, mean_absolute_error: 0.3192, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7539, number_of_instances: 768, precision: 0.7477, predictive_accuracy: 0.7539, prior_entropy: 0.9331, relative_absolute_error: 0.7024, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.401, root_relative_squared_error: 0.8413, unweighted_recall: 0.7106,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8247, f_measure: 0.7568, kappa: 0.4573, kb_relative_information_score: 0.3059, mean_absolute_error: 0.3199, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7617, number_of_instances: 768, precision: 0.7562, predictive_accuracy: 0.7617, prior_entropy: 0.9331, relative_absolute_error: 0.704, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4008, root_relative_squared_error: 0.8409, unweighted_recall: 0.7209,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8294, f_measure: 0.7754, kappa: 0.4988, kb_relative_information_score: 0.3164, mean_absolute_error: 0.316, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7799, number_of_instances: 768, precision: 0.7753, predictive_accuracy: 0.7799, prior_entropy: 0.9331, relative_absolute_error: 0.6953, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3976, root_relative_squared_error: 0.8342, unweighted_recall: 0.741,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8265, f_measure: 0.7684, kappa: 0.4826, kb_relative_information_score: 0.3104, mean_absolute_error: 0.3192, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7734, number_of_instances: 768, precision: 0.7684, predictive_accuracy: 0.7734, prior_entropy: 0.9331, relative_absolute_error: 0.7023, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3993, root_relative_squared_error: 0.8378, unweighted_recall: 0.7325,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8256, f_measure: 0.7529, kappa: 0.4474, kb_relative_information_score: 0.3084, mean_absolute_error: 0.3193, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7591, number_of_instances: 768, precision: 0.753, predictive_accuracy: 0.7591, prior_entropy: 0.9331, relative_absolute_error: 0.7025, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4008, root_relative_squared_error: 0.8409, unweighted_recall: 0.7146,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8281, f_measure: 0.7622, kappa: 0.4691, kb_relative_information_score: 0.3095, mean_absolute_error: 0.3194, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7669, number_of_instances: 768, precision: 0.7617, predictive_accuracy: 0.7669, prior_entropy: 0.9331, relative_absolute_error: 0.7028, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3988, root_relative_squared_error: 0.8366, unweighted_recall: 0.7266,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8268, f_measure: 0.7613, kappa: 0.4676, kb_relative_information_score: 0.3122, mean_absolute_error: 0.3176, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7656, number_of_instances: 768, precision: 0.7605, predictive_accuracy: 0.7656, prior_entropy: 0.9331, relative_absolute_error: 0.6987, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3992, root_relative_squared_error: 0.8374, unweighted_recall: 0.7265,
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69565 instances - 8 features - classes - 901 missing values
Predict the stock prices of Microsoft Corporation using LSTM.
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1925 instances - 7 features - classes - 0 missing values
A dataset for network intrusion detection.
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A dataset for network intrusion detection.
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25500 instances - 46 features - 0 classes - 0 missing values
weather data
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145460 instances - 23 features - classes - 343248 missing values
data
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145460 instances - 23 features - classes - 343248 missing values
data
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145460 instances - 23 features - classes - 343248 missing values
data
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145460 instances - 23 features - classes - 343248 missing values
data
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145460 instances - 23 features - classes - 343248 missing values
Training data for ai
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2258 instances - 2 features - classes - 0 missing values
meta features with best models
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75 instances - 32 features - 10 classes - 0 missing values
meta features with best model
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75 instances - 32 features - 0 classes - 0 missing values
meta features with best model
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75 instances - 32 features - 10 classes - 0 missing values
Meta features classified with best models
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75 instances - 32 features - 10 classes - 0 missing values
Meta features classified with best models
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75 instances - 32 features - 10 classes - 0 missing values
Automl_meta_feat
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75 instances - 32 features - classes - 0 missing values
A dataset for network intrusion detection.
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A dataset for network intrusion detection.
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54296 instances - 46 features - 0 classes - 0 missing values
This is a collection of datasets that can be used to evaluate Confidence Interval methods for the Generalization Error. The task splits can be ignored. For more information, see…
18 datasets, 18 tasks, 0 flows, 0 runs
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uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : Target
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : class
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : SLUMP_cm,FLOW_cm,Compressive_Strength_Mpa
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : reference
From original source: ----- The dataset consists of 384 features extracted from CT images. The class variable is numeric and denotes the relative location of the CT slice on the axial axis of the…
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53500 instances - 385 features - 0 classes - 0 missing values
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : Compressive_Strength_Mpa
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : FLOW_cm
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : SLUMP_cm
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : S
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : stab
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : stabf
From original source: ----- The local stability analysis of the 4-node star system (electricity producer is in the center) implementing Decentral Smart Grid Control concept. Additional Information The…
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10000 instances - 13 features - 0 classes - 0 missing values
From original source: ----- The local stability analysis of the 4-node star system (electricity producer is in the center) implementing Decentral Smart Grid Control concept. Additional Information The…
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10000 instances - 13 features - 2 classes - 0 missing values
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : class
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : Revenue
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : class
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : Team_won
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : Classification
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : 13
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : 8
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : LONGITUDE
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : LATITUDE
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : 4
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : pm2.5
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : charges
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : 23
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : price
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : 9
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : Appliances
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : 127
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : target
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : Classes
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : Churn
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uploader_id : 30703 - estimation_procedure : 10-fold Crossvalidation - target_feature : target
From original source: ----- The dataset contains count of public bicycles rented per hour in the Seoul Bike Sharing System, with corresponding weather data and holiday information Additional…
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8760 instances - 14 features - 0 classes - 0 missing values
From original source: ----- Data set containing values for 8 attributes (molecular descriptors) of 546 chemicals used to predict quantitative acute aquatic toxicity towards Daphnia Magna.. Additional…
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546 instances - 9 features - 0 classes - 0 missing values
From original source: ----- The UJIIndoorLoc is a Multi-Building Multi-Floor indoor localization database to test Indoor Positioning System that rely on WLAN/WiFi fingerprint. Additional Information…
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21048 instances - 526 features - 0 classes - 0 missing values
From original source: ----- The UJIIndoorLoc is a Multi-Building Multi-Floor indoor localization database to test Indoor Positioning System that rely on WLAN/WiFi fingerprint. Additional Information…
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21048 instances - 526 features - 0 classes - 0 missing values
From original source: ----- Data was from a simulation of a servo system Additional Information Ross Quinlan: This data was given to me by Karl Ulrich at MIT in 1986. I didn't record his description…
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167 instances - 5 features - 0 classes - 0 missing values
From original source: ----- Context Machine Learning with R by Brett Lantz is a book that provides an introduction to machine learning using R. As far as I can tell, Packt Publishing does not make its…
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1338 instances - 7 features - 0 classes - 0 missing values
From original source: ----- KEGG Metabolic pathways modeled as directed relation network. Variety of graphical features presented. Additional Information KEGG Metabolic pathways can be realized into…
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53413 instances - 23 features - 0 classes - 0 missing values
From original source: ----- Communities within the United States. The data combines socio-economic data from the 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crime data from…
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209 instances - 9 features - 0 classes - 0 missing values
From original source: ----- Communities within the United States. The data combines socio-economic data from the 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crime data from…
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1994 instances - 123 features - 0 classes - 36851 missing values
From original source: ----- This hourly data set contains the PM2.5 data of US Embassy in Beijing. Meanwhile, meteorological data from Beijing Capital International Airport are also included.…
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41757 instances - 12 features - 0 classes - 0 missing values
From original source: ----- About this file What are the things that a potential home buyer considers before purchasing a house? The location, the size of the property, vicinity to offices, schools,…
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13320 instances - 8 features - 0 classes - 682 missing values