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Supervised Regression on liver-disorders

Supervised Regression on liver-disorders

Task 52948 Supervised Regression liver-disorders 122 runs submitted
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PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... ession.KNeighborsRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(3) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(3) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(3) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(1) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(3) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(4) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(4) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(4) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(4) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(4) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,
PAVAN KUMAR PERUGU 1 year ago
ran flow sklearn.pipeline.Pipeline(colu ... sses.DecisionTreeRegressor)(4) on task Supervised Regression on data set liver-disorders
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 2.623, number_of_instances: 345, root_mean_prior_squared_error: 3.333,

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Leaderboard

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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 root_mean_squared_error
source_data liver-disorders (1)
target_feature drinks

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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