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Supervised Regression on analcatdata_negotiation

Task 4823 Supervised Regression analcatdata_negotiation 53 runs submitted
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53 results

Koralp Catalsakal 5 years ago
ran flow Framework.custom_sklearn_pipel ... ing_blocks.ProcessingBlock)(1) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.7482, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 0.8756, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 1.0006, root_relative_squared_error: 0.8926,

Koralp Catalsakal 5 years ago
ran flow sklearn.pipeline.Pipeline(Shap ... peline.CustomPipelineModel)(1) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.6959, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 0.8145, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 0.9441, root_relative_squared_error: 0.8422,

Koralp Catalsakal 5 years ago
ran flow sklearn.pipeline.Pipeline(Shap ... peline.CustomPipelineModel)(1) on task Supervised Regression on data set analcatdata_negotiation

Martin Hoang 6 years ago
ran flow mlr.regr.rpart(6) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.6269, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 0.7336, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 0.8863, root_relative_squared_error: 0.7907, usercpu_time_millis: 0.08, usercpu_time_millis_testing: 0.02, usercpu_time_millis_training: 0.06,

Adrian-Stefan Mares 6 years ago
ran flow torch.nn.modules.container.Sequential.5c70f9acfe1da095(1) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 6.0118, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 7.036, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 6.1157, root_relative_squared_error: 5.4556,

Adrian-Stefan Mares 6 years ago
ran flow torch.nn.modules.container.Sequential.61aac54ec3515ad0(1) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 5.9348, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 6.9458, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 6.0397, root_relative_squared_error: 5.3879,

Adrian-Stefan Mares 6 years ago
ran flow torch.nn.modules.container.Sequential.da2234e717eb3f27(1) on task Supervised Regression on data set analcatdata_negotiation

Adrian-Stefan Mares 6 years ago
ran flow torch.nn.modules.container.Sequential.21eabd90fbdbc309(1) on task Supervised Regression on data set analcatdata_negotiation

Nico Strasdat 6 years ago
ran flow sklearn.pipeline.Pipeline(miss ... hpo4svm.hpo.sklearn.HPOSVM)(1) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5776, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 0.676, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 0.7891, root_relative_squared_error: 0.7039,

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.8573, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 1.0033, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 1.1124, root_relative_squared_error: 0.9923,

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5838, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 0.6833, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 0.8319, root_relative_squared_error: 0.7421,

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.9301, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 1.0886, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 1.1966, root_relative_squared_error: 1.0675,

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5979, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 0.6997, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 0.8798, root_relative_squared_error: 0.7848,

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5828, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 0.682, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 0.831, root_relative_squared_error: 0.7413,

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5766, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 0.6748, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 0.8306, root_relative_squared_error: 0.741,

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_negotiation

0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5904, mean_prior_absolute_error: 0.8544, number_of_instances: 92, relative_absolute_error: 0.691, root_mean_prior_squared_error: 1.121, root_mean_squared_error: 0.8904, root_relative_squared_error: 0.7943,

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(5) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(4) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(3) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(3) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(3) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(3) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.cforest(3) on task Supervised Regression on data set analcatdata_negotiation

Heidi Seibold 8 years ago
ran flow mlr.regr.develpartykit.ctree(3) on task Supervised Regression on data set analcatdata_negotiation

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Leaderboard

Rank	Name	Top Score	Entries	Highest rank

Note: The leaderboard ignores resubmissions of previous solutions, as well as parameter variations that do not improve performance.

Challenge

Given a dataset with a numeric target and a set of train/test splits, e.g. generated by a cross-validation procedure, train a model and return the predictions of that model.

Given inputs

estimation_procedure	10-fold Crossvalidation
evaluation_measures	mean_absolute_error
source_data	analcatdata_negotiation (1)
target_feature	Future_business

Expected outputs

evaluations	A list of user-defined evaluations of the task as key-value pairs.	KeyValue (optional)
model	A file containing the model built on all the input data.	File (optional)
predictions	The desired output format	Predictions (optional)

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Supervised Regression on analcatdata_negotiation

53 Runs

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