OpenML
For search options, select a result type first
10430401 results
Filter results by:
Supervised Classification on simulated_covertype
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : Y
Supervised Classification on simulated_bank_marketing
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : Class
Supervised Classification on simulated_adult
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : class
Supervised Classification on prostate
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Regression on chen_10
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Regression on chen_10_null
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Classification on breast
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Classification on bates_classif_100
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Regression on bates_regr_100
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Regression on friedman1
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Classification on colon
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Regression on bates_regr_20
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Classification on bates_classif_20
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
Supervised Regression on test
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : Target
Supervised Classification on test
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : Species
Supervised Regression on test
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : Species
Supervised Regression on test
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 30127 - estimation_procedure : 10-fold Crossvalidation - target_feature : Species
a (2)
a
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
prostate (2)
More information coming soon
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 103 features - 2 classes - 0 missing values
chen_10 (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 61 features - 0 classes - 0 missing values
chen_10_null (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 61 features - 0 classes - 0 missing values
breast (4)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 78 features - 2 classes - 0 missing values
bates_classif_100 (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 101 features - 2 classes - 0 missing values
bates_regr_100 (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 101 features - 0 classes - 0 missing values
Md Moniruzzaman 1 year ago
ran flow sklearn.pipeline.Pipeline(impu ... es.DecisionTreeClassifier)(29) on task Supervised Classification on data set pendigits
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9795, f_measure: 0.9632, kappa: 0.9591, kb_relative_information_score: 0.9615, mean_absolute_error: 0.0074, mean_prior_absolute_error: 0.18, weighted_recall: 0.9632, number_of_instances: 10992, precision: 0.9632, predictive_accuracy: 0.9632, prior_entropy: 3.3208, relative_absolute_error: 0.0409, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.0858, root_relative_squared_error: 0.2862, unweighted_recall: 0.9631,
Md Moniruzzaman 1 year ago
ran flow sklearn.pipeline.Pipeline(impu ... es.DecisionTreeClassifier)(29) on task Supervised Classification on data set pendigits
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.979, f_measure: 0.9622, kappa: 0.9579, kb_relative_information_score: 0.9604, mean_absolute_error: 0.0076, mean_prior_absolute_error: 0.18, weighted_recall: 0.9622, number_of_instances: 10992, precision: 0.9622, predictive_accuracy: 0.9622, prior_entropy: 3.3208, relative_absolute_error: 0.0421, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.087, root_relative_squared_error: 0.29, unweighted_recall: 0.9621,
Classification for Tabular data related to MI
Classification/Risk prediction for Tabular data related to MI(Coronary heart disease)
1 datasets, 1 tasks, 0 flows, 0 runs
friedman1 (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 11 features - 0 classes - 0 missing values
colon (2)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 63 features - 2 classes - 0 missing values
subset_higgs (2)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 29 features - 0 classes - 0 missing values
simulated_video_transcoding (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 19 features - 0 classes - 3 missing values
simulated_sgemm_gpu_kernel_performance (2)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 15 features - 0 classes - 0 missing values
simulated_physiochemical_protein (2)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 10 features - 0 classes - 0 missing values
simulated_electricity (2)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 9 features - 2 classes - 0 missing values
simulated_diamonds (2)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 10 features - 0 classes - 34 missing values
simulated_covertype (2)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 11 features - 0 classes - 0 missing values
simulated_bank_marketing (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 17 features - 0 classes - 76 missing values
simulated_adult (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 15 features - 2 classes - 68 missing values
bates_regr_20 (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 21 features - 0 classes - 0 missing values
bates_classif_20 (1)
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 21 features - 2 classes - 0 missing values
test (15)
Myocardial infarction complications Database
0 runs0 likes0 downloads0 reach0 impact
30 instances - 5 features - 0 classes - 0 missing values
test (14)
Myocardial infarction complications Database
0 runs0 likes0 downloads0 reach0 impact
30 instances - 5 features - 0 classes - 0 missing values
test (13)
Myocardial infarction complications Database
0 runs0 likes0 downloads0 reach0 impact
30 instances - 6 features - 0 classes - 0 missing values
test (12)
Myocardial infarction complications Database
0 runs0 likes0 downloads0 reach0 impact
30 instances - 6 features - 0 classes - 0 missing values
Matthias Schartner 1 year ago
ran flow sklearn.pipeline.Pipeline(impu ... est.RandomForestClassifier)(1) on task Supervised Classification on data set higgs
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8027, f_measure: 0.7241, kappa: 0.4461, kb_relative_information_score: 0.2423, mean_absolute_error: 0.393, mean_prior_absolute_error: 0.4984, weighted_recall: 0.7243, number_of_instances: 98050, precision: 0.724, predictive_accuracy: 0.7243, prior_entropy: 0.9976, relative_absolute_error: 0.7885, root_mean_prior_squared_error: 0.4992, root_mean_squared_error: 0.4298, root_relative_squared_error: 0.8609, unweighted_recall: 0.7228,
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler,clf=sklearn.ensemble._forest.RandomForestClassifier) (1)
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
Supervised Classification on MIC
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37600 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : LET_IS
Supervised Classification on BNG(cylinder-bands,nominal,1000000)
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37600 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Classification for Tabular data related to MI
Classification/Risk prediction for Tabular data related to MI(Coronary heart disease)
1 datasets, 1 tasks, 0 flows, 0 runs
MIC (1)
Myocardial infarction complications Database
0 runs0 likes0 downloads0 reach0 impact
1649 instances - 105 features - 2 classes - 0 missing values
MidSize Suite
illustrating how to create a benchmark suite
2 datasets, 2 tasks, 0 flows, 0 runs
MidSize Suite
illustrating how to create a benchmark suite
2 datasets, 2 tasks, 0 flows, 0 runs
MidSize Suite
illustrating how to create a benchmark suite
2 datasets, 2 tasks, 0 flows, 0 runs
MidSize Suite
illustrating how to create a benchmark suite
250 datasets, 250 tasks, 0 flows, 0 runs
Anders Viklund 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(32) on task Supervised Classification on data set blood-transfusion-service-center
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6899, f_measure: 0.7235, kappa: 0.2005, kb_relative_information_score: 0.0937, mean_absolute_error: 0.3023, mean_prior_absolute_error: 0.363, weighted_recall: 0.742, number_of_instances: 748, precision: 0.7143, predictive_accuracy: 0.742, prior_entropy: 0.7916, relative_absolute_error: 0.8327, root_mean_prior_squared_error: 0.4258, root_mean_squared_error: 0.427, root_relative_squared_error: 1.0026, unweighted_recall: 0.5892,
vonfry-test-collection
test
1 datasets, 1 tasks, 0 flows, 0 runs
fry von 1 year ago
ran flow vonfry-test-flow(1) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9838, f_measure: 0.9332, kappa: 0.8648, kb_relative_information_score: 0.6372, mean_absolute_error: 0.2027, mean_prior_absolute_error: 0.4948, weighted_recall: 0.9334, number_of_instances: 14980, precision: 0.9338, predictive_accuracy: 0.9334, prior_entropy: 0.9924, relative_absolute_error: 0.4097, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.2614, root_relative_squared_error: 0.5255, unweighted_recall: 0.9306,
vonfry-test-flow (1)
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
Supervised Classification on credit-g
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37411 - estimation_procedure : 10-fold Crossvalidation - target_feature : vonfry-test
Allen Pan 1 year ago
ran flow sklearn.neighbors._classification.KNeighborsClassifier(22) on task Supervised Classification on data set iris
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9873, f_measure: 0.9667, kappa: 0.95, kb_relative_information_score: 0.9338, mean_absolute_error: 0.0356, mean_prior_absolute_error: 0.4444, weighted_recall: 0.9667, number_of_instances: 150, precision: 0.9668, predictive_accuracy: 0.9667, prior_entropy: 1.585, relative_absolute_error: 0.08, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1424, root_relative_squared_error: 0.302, unweighted_recall: 0.9667,
sklearn.neighbors._classification.KNeighborsClassifier (22)
Classifier implementing the k-nearest neighbors vote.
1 runs0 likes0 downloads0 reach0 impact
Allen Pan 1 year ago
ran flow sklearn.neighbors._classification.KNeighborsClassifier(21) on task Supervised Classification on data set iris
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9873, f_measure: 0.9667, kappa: 0.95, kb_relative_information_score: 0.9338, mean_absolute_error: 0.0356, mean_prior_absolute_error: 0.4444, weighted_recall: 0.9667, number_of_instances: 150, precision: 0.9668, predictive_accuracy: 0.9667, prior_entropy: 1.585, relative_absolute_error: 0.08, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1424, root_relative_squared_error: 0.302, unweighted_recall: 0.9667,
Supervised Regression on rossmann_store_sales
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37379 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : Sales
rossmann_store_sales (1)
Rossmann Store Sales from Kaggle with some pre-processing
0 runs0 likes0 downloads0 reach0 impact
804056 instances - 18 features - 0 classes - 0 missing values
Pakarat Musikawan 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(32) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9838, f_measure: 0.9315, kappa: 0.8612, kb_relative_information_score: 0.6353, mean_absolute_error: 0.2036, mean_prior_absolute_error: 0.4948, weighted_recall: 0.9316, number_of_instances: 14980, precision: 0.9321, predictive_accuracy: 0.9316, prior_entropy: 0.9924, relative_absolute_error: 0.4115, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.2621, root_relative_squared_error: 0.527, unweighted_recall: 0.9287,
Jan van Rijn 1 year ago
ran flow sklearn.neighbors._classification.KNeighborsClassifier(21) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9947, f_measure: 0.969, kappa: 0.9374, kb_relative_information_score: 0.8914, mean_absolute_error: 0.0579, mean_prior_absolute_error: 0.4948, weighted_recall: 0.969, number_of_instances: 14980, precision: 0.969, predictive_accuracy: 0.969, prior_entropy: 0.9924, relative_absolute_error: 0.117, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.159, root_relative_squared_error: 0.3196, unweighted_recall: 0.9684,
Nuttapong Chimwai 1 year ago
ran flow sklearn.neighbors._classification.KNeighborsClassifier(21) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9931, f_measure: 0.9747, kappa: 0.9489, kb_relative_information_score: 0.9177, mean_absolute_error: 0.0428, mean_prior_absolute_error: 0.4948, weighted_recall: 0.9747, number_of_instances: 14980, precision: 0.9747, predictive_accuracy: 0.9747, prior_entropy: 0.9924, relative_absolute_error: 0.0865, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.1478, root_relative_squared_error: 0.2972, unweighted_recall: 0.9744,
sklearn.neighbors._classification.KNeighborsClassifier (21)
Classifier implementing the k-nearest neighbors vote.
3 runs0 likes0 downloads0 reach2 impact
Mamang Duniang 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(34) on task Supervised Classification on data set MagicTelescope
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9367, f_measure: 0.8791, kappa: 0.7316, kb_relative_information_score: 0.598, mean_absolute_error: 0.194, mean_prior_absolute_error: 0.456, weighted_recall: 0.8811, number_of_instances: 19020, precision: 0.8809, predictive_accuracy: 0.8811, prior_entropy: 0.9355, relative_absolute_error: 0.4255, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2988, root_relative_squared_error: 0.6257, unweighted_recall: 0.8554,
Mamang Duniang 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(34) on task Supervised Classification on data set MagicTelescope
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9366, f_measure: 0.8797, kappa: 0.7331, kb_relative_information_score: 0.5974, mean_absolute_error: 0.1943, mean_prior_absolute_error: 0.456, weighted_recall: 0.8817, number_of_instances: 19020, precision: 0.8814, predictive_accuracy: 0.8817, prior_entropy: 0.9355, relative_absolute_error: 0.4262, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.299, root_relative_squared_error: 0.6261, unweighted_recall: 0.8564,
Suraphum Maneeratchakit 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(32) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9828, f_measure: 0.9324, kappa: 0.8631, kb_relative_information_score: 0.6357, mean_absolute_error: 0.2031, mean_prior_absolute_error: 0.4948, weighted_recall: 0.9326, number_of_instances: 14980, precision: 0.9331, predictive_accuracy: 0.9326, prior_entropy: 0.9924, relative_absolute_error: 0.4105, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.2631, root_relative_squared_error: 0.5289, unweighted_recall: 0.9297,
Theeraphat Seetha 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(32) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9824, f_measure: 0.9304, kappa: 0.859, kb_relative_information_score: 0.6362, mean_absolute_error: 0.2027, mean_prior_absolute_error: 0.4948, weighted_recall: 0.9306, number_of_instances: 14980, precision: 0.9311, predictive_accuracy: 0.9306, prior_entropy: 0.9924, relative_absolute_error: 0.4097, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.2633, root_relative_squared_error: 0.5293, unweighted_recall: 0.9276,
Jan van Rijn 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(32) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9826, f_measure: 0.929, kappa: 0.8563, kb_relative_information_score: 0.635, mean_absolute_error: 0.2033, mean_prior_absolute_error: 0.4948, weighted_recall: 0.9292, number_of_instances: 14980, precision: 0.9298, predictive_accuracy: 0.9292, prior_entropy: 0.9924, relative_absolute_error: 0.4109, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.2635, root_relative_squared_error: 0.5298, unweighted_recall: 0.9261,
Weather (51)
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
Jan van Rijn 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(32) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9826, f_measure: 0.9298, kappa: 0.8579, kb_relative_information_score: 0.636, mean_absolute_error: 0.203, mean_prior_absolute_error: 0.4948, weighted_recall: 0.93, number_of_instances: 14980, precision: 0.9306, predictive_accuracy: 0.93, prior_entropy: 0.9924, relative_absolute_error: 0.4102, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.2629, root_relative_squared_error: 0.5287, unweighted_recall: 0.9269,
Theeraphat Seetha 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(32) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9823, f_measure: 0.9301, kappa: 0.8585, kb_relative_information_score: 0.6358, mean_absolute_error: 0.2028, mean_prior_absolute_error: 0.4948, weighted_recall: 0.9303, number_of_instances: 14980, precision: 0.9308, predictive_accuracy: 0.9303, prior_entropy: 0.9924, relative_absolute_error: 0.41, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.2636, root_relative_squared_error: 0.53, unweighted_recall: 0.9273,
Jan van Rijn 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(32) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9831, f_measure: 0.9327, kappa: 0.8638, kb_relative_information_score: 0.636, mean_absolute_error: 0.2031, mean_prior_absolute_error: 0.4948, weighted_recall: 0.9329, number_of_instances: 14980, precision: 0.9334, predictive_accuracy: 0.9329, prior_entropy: 0.9924, relative_absolute_error: 0.4105, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.2626, root_relative_squared_error: 0.5279, unweighted_recall: 0.93,
Jan van Rijn 1 year ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(34) on task Supervised Classification on data set eeg-eye-state
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9827, f_measure: 0.9294, kappa: 0.8571, kb_relative_information_score: 0.6365, mean_absolute_error: 0.2025, mean_prior_absolute_error: 0.4948, weighted_recall: 0.9296, number_of_instances: 14980, precision: 0.9302, predictive_accuracy: 0.9296, prior_entropy: 0.9924, relative_absolute_error: 0.4094, root_mean_prior_squared_error: 0.4974, root_mean_squared_error: 0.2628, root_relative_squared_error: 0.5284, unweighted_recall: 0.9266,
Weather (50)
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
Jakkrapun Klangkankullapun 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... est.RandomForestClassifier)(1) on task Supervised Classification on data set Weather
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6222, f_measure: 0.7143, kappa: 0.3778, kb_relative_information_score: 0.1028, mean_absolute_error: 0.42, 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: 0.9046, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4844, root_relative_squared_error: 1.0103, unweighted_recall: 0.6889,
Supervised Classification on Weather
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37230 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
Weather (49)
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
Kittikan Charoenrat 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... est.RandomForestClassifier)(1) on task Supervised Classification on data set Weather
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6222, f_measure: 0.7794, kappa: 0.5116, kb_relative_information_score: 0.1002, 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: 0.4858, root_relative_squared_error: 1.0132, unweighted_recall: 0.7444,
Supervised Classification on Weather
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37222 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
Jakkrapun Klangkankullapun 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... est.RandomForestClassifier)(1) on task Supervised Classification on data set Weather
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6, f_measure: 0.6324, kappa: 0.186, kb_relative_information_score: 0.0345, mean_absolute_error: 0.4436, 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: 0.9554, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.5077, root_relative_squared_error: 1.0588, unweighted_recall: 0.5889,
Supervised Classification on Weather
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37230 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
Weather (48)
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
Tananon Klinkaew 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... est.RandomForestClassifier)(1) on task Supervised Classification on data set Weather
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6222, f_measure: 0.5918, kappa: 0.1026, kb_relative_information_score: 0.0919, mean_absolute_error: 0.4243, mean_prior_absolute_error: 0.4643, weighted_recall: 0.6429, number_of_instances: 14, precision: 0.6071, predictive_accuracy: 0.6429, prior_entropy: 0.9413, relative_absolute_error: 0.9138, root_mean_prior_squared_error: 0.4795, root_mean_squared_error: 0.4934, root_relative_squared_error: 1.0289, unweighted_recall: 0.5444,
Weather (47)
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
Supervised Classification on Weather
1 runs0 likes0 downloads0 reach0 impact
uploader_id : 37223 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
Weather (46)
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
Supervised Classification on Weather
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 37230 - estimation_procedure : 10-fold Crossvalidation - target_feature : play
Weather (45)
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
Test
1)BUY #BANKNIFTY 45300 PE ABOVE -490 TARGET- 40 /70/100/200/250 Point SL-450 2)BUY #BANKNIFTY 45300 PE ABOVE -550 TARGET- 40 /70/100/200/250 Point SL-500 3)BUY#ALKEM 4100 CE ABOVE -218 TARGE- 228,250…
1 datasets, 1 tasks, 0 flows, 0 runs
Test
1)BUY #BANKNIFTY 45300 PE ABOVE -490 TARGET- 40 /70/100/200/250 Point SL-450 2)BUY #BANKNIFTY 45300 PE ABOVE -550 TARGET- 40 /70/100/200/250 Point SL-500 3)BUY#ALKEM 4100 CE ABOVE -218 TARGE- 228,250…
1 datasets, 1 tasks, 0 flows, 0 runs
said edsa 1 year ago
ran flow sklearn.pipeline.Pipeline(vari ... est.RandomForestClassifier)(1) on task Supervised Classification on data set appendicitis_test_edsa
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8188, f_measure: 0.8675, kappa: 0.5664, kb_relative_information_score: 0.3369, mean_absolute_error: 0.1957, mean_prior_absolute_error: 0.3211, weighted_recall: 0.8736, number_of_instances: 1060, precision: 0.8663, predictive_accuracy: 0.8736, prior_entropy: 0.7183, relative_absolute_error: 0.6093, root_mean_prior_squared_error: 0.3986, root_mean_squared_error: 0.3293, root_relative_squared_error: 0.826, unweighted_recall: 0.7598,
sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier) (1)
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
Weather (44)
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
Weather (43)
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
Nuttakit Thaiphuttra 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... est.RandomForestClassifier)(3) on task Supervised Classification on data set Weather
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6222, f_measure: 0.7794, kappa: 0.5116, kb_relative_information_score: 0.0812, 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: 0.4872, root_relative_squared_error: 1.0161, unweighted_recall: 0.7444,