DEVELOPMENT...

A PHP Error was encountered

Severity: Warning

Message: fopen(/data/log/sql.log): failed to open stream: Permission denied

Filename: models/Log.php

Line Number: 37

Backtrace:

File: /var/www/openml/openml_OS/models/Log.php
Line: 37
Function: fopen

File: /var/www/openml/openml_OS/core/MY_Database_Read_Model.php
Line: 30
Function: sql

File: /var/www/openml/openml_OS/views/pages/frontend/t/pre.php
Line: 104
Function: query

File: /var/www/openml/openml_OS/helpers/cms_helper.php
Line: 19
Function: view

File: /var/www/openml/openml_OS/controllers/Frontend.php
Line: 85
Function: loadpage

File: /var/www/openml/index.php
Line: 337
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: fopen(/data/log/sql.log): failed to open stream: Permission denied

Filename: models/Log.php

Line Number: 37

Backtrace:

File: /var/www/openml/openml_OS/models/Log.php
Line: 37
Function: fopen

File: /var/www/openml/openml_OS/core/MY_Database_Read_Model.php
Line: 30
Function: sql

File: /var/www/openml/openml_OS/views/pages/frontend/t/pre.php
Line: 124
Function: query

File: /var/www/openml/openml_OS/helpers/cms_helper.php
Line: 19
Function: view

File: /var/www/openml/openml_OS/controllers/Frontend.php
Line: 85
Function: loadpage

File: /var/www/openml/index.php
Line: 337
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: fopen(/data/log/sql.log): failed to open stream: Permission denied

Filename: models/Log.php

Line Number: 37

Backtrace:

File: /var/www/openml/openml_OS/models/Log.php
Line: 37
Function: fopen

File: /var/www/openml/openml_OS/core/MY_Database_Read_Model.php
Line: 30
Function: sql

File: /var/www/openml/openml_OS/views/pages/frontend/t/pre.php
Line: 117
Function: query

File: /var/www/openml/openml_OS/helpers/cms_helper.php
Line: 19
Function: view

File: /var/www/openml/openml_OS/controllers/Frontend.php
Line: 85
Function: loadpage

File: /var/www/openml/index.php
Line: 337
Function: require_once

OpenML
OpenML
Supervised Classification on autos

Supervised Classification on autos

Task 9 Supervised Classification autos 1382 runs submitted
0 likes downloaded by 0 people , 0 total downloads 0 issues
Visibility: Public
  • basic study_1 study_107 study_41 study_73 under100k under1m
Issue #Downvotes for this reason By


Metric:

1382 Runs

Fetching data
Note: Chart is limited to the 100 best flows, and the 100 best runs for each flow.
Fetching data
Search runs in more detail
ID's
Sort: most recent
422 results
run
Felicia West 11 years ago
ran flow weka.SMO_PolyKernel(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5417, f_measure: 0.2534, kappa: 0.0868, kb_relative_information_score: 4.6504, mean_absolute_error: 0.2234, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.2869, predictive_accuracy: 0.3805, prior_entropy: 2.3268, recall: 0.3805, relative_absolute_error: 1.0112, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.3317, root_relative_squared_error: 0.9998, scimark_benchmark: 909.1174,
Felicia West 11 years ago
ran flow weka.J48(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8116, f_measure: 0.5857, kappa: 0.4797, kb_relative_information_score: 89.8851, mean_absolute_error: 0.1437, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.6169, predictive_accuracy: 0.6146, prior_entropy: 2.3268, recall: 0.6146, relative_absolute_error: 0.6507, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2799, root_relative_squared_error: 0.8436, scimark_benchmark: 947.9276,
Felicia West 11 years ago
ran flow weka.ClassificationViaRegression_M5P(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9047, f_measure: 0.7607, kappa: 0.6968, kb_relative_information_score: 123.8744, mean_absolute_error: 0.1139, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.7586, predictive_accuracy: 0.7659, prior_entropy: 2.3268, recall: 0.7659, relative_absolute_error: 0.5157, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2371, root_relative_squared_error: 0.7147, scimark_benchmark: 946.1314,
Felicia West 11 years ago
ran flow weka.SMO_RBFKernel(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, f_measure: 0.161, kb_relative_information_score: 3.7986, mean_absolute_error: 0.2241, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.1068, predictive_accuracy: 0.3268, prior_entropy: 2.3268, recall: 0.3268, relative_absolute_error: 1.0145, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.3328, root_relative_squared_error: 1.0031, scimark_benchmark: 947.1439,
Felicia West 11 years ago
ran flow weka.SMO_RBFKernel(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6989, f_measure: 0.5489, kappa: 0.3924, kb_relative_information_score: 17.7959, mean_absolute_error: 0.2174, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.8144, predictive_accuracy: 0.5707, prior_entropy: 2.3268, recall: 0.5707, relative_absolute_error: 0.9839, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.3225, root_relative_squared_error: 0.972, scimark_benchmark: 949.7268,
Felicia West 11 years ago
ran flow weka.J48(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9049, f_measure: 0.8084, kappa: 0.7517, kb_relative_information_score: 149.9815, mean_absolute_error: 0.0673, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.812, predictive_accuracy: 0.8098, prior_entropy: 2.3268, recall: 0.8098, relative_absolute_error: 0.3045, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2244, root_relative_squared_error: 0.6765, scimark_benchmark: 945.4612,
Felicia West 11 years ago
ran flow weka.J48(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8067, f_measure: 0.5528, kappa: 0.4327, kb_relative_information_score: 91.5998, mean_absolute_error: 0.1524, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.5513, predictive_accuracy: 0.5659, prior_entropy: 2.3268, recall: 0.5659, relative_absolute_error: 0.69, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2874, root_relative_squared_error: 0.8664, scimark_benchmark: 948.9091,
Felicia West 11 years ago
ran flow weka.IBk(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8574, f_measure: 0.6204, kappa: 0.5091, kb_relative_information_score: 90.995, mean_absolute_error: 0.1548, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.6205, predictive_accuracy: 0.6293, prior_entropy: 2.3268, recall: 0.6293, relative_absolute_error: 0.7006, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.279, root_relative_squared_error: 0.8409, scimark_benchmark: 943.7846,
Felicia West 11 years ago
ran flow weka.SMO_RBFKernel(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8643, f_measure: 0.6639, kappa: 0.5681, kb_relative_information_score: 34.9555, mean_absolute_error: 0.2105, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.665, predictive_accuracy: 0.6732, prior_entropy: 2.3268, recall: 0.6732, relative_absolute_error: 0.9529, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.3117, root_relative_squared_error: 0.9394, scimark_benchmark: 943.2491,
Felicia West 11 years ago
ran flow weka.MultiClassClassifier_Logistic(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8334, f_measure: 0.6802, kappa: 0.5672, kb_relative_information_score: 4.9709, mean_absolute_error: 0.2367, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.7116, predictive_accuracy: 0.6585, prior_entropy: 2.3268, recall: 0.6585, relative_absolute_error: 1.0712, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.3386, root_relative_squared_error: 1.0205, scimark_benchmark: 944.9536,
Felicia West 11 years ago
ran flow weka.SMO_RBFKernel(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7124, f_measure: 0.5542, kappa: 0.3984, kb_relative_information_score: 16.914, mean_absolute_error: 0.2173, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.8007, predictive_accuracy: 0.5756, prior_entropy: 2.3268, recall: 0.5756, relative_absolute_error: 0.9836, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.3224, root_relative_squared_error: 0.9716, scimark_benchmark: 939.2926,
Felicia West 11 years ago
ran flow weka.LMT(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9036, f_measure: 0.7407, kappa: 0.6626, kb_relative_information_score: 135.0769, mean_absolute_error: 0.0886, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.7448, predictive_accuracy: 0.7415, prior_entropy: 2.3268, recall: 0.7415, relative_absolute_error: 0.4009, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2367, root_relative_squared_error: 0.7134, scimark_benchmark: 945.721,
Felicia West 11 years ago
ran flow weka.CVParameterSelection_ZeroR(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4638, f_measure: 0.161, kb_relative_information_score: 0.2693, mean_absolute_error: 0.2211, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.1068, predictive_accuracy: 0.3268, prior_entropy: 2.3268, recall: 0.3268, relative_absolute_error: 1.0008, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.3319, root_relative_squared_error: 1.0004, scimark_benchmark: 940.3721,
Jason 11 years ago
ran flow weka.IBk(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8264, f_measure: 0.7403, kappa: 0.6606, kb_relative_information_score: 134.7304, mean_absolute_error: 0.0815, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.7502, predictive_accuracy: 0.7366, prior_entropy: 2.3268, recall: 0.7366, relative_absolute_error: 0.3687, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2696, root_relative_squared_error: 0.8126, scimark_benchmark: 2013.3601,
Jason 11 years ago
ran flow weka.Logistic(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8389, f_measure: 0.7028, kappa: 0.6132, kb_relative_information_score: 129.8947, mean_absolute_error: 0.0859, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.7045, predictive_accuracy: 0.7024, prior_entropy: 2.3268, recall: 0.7024, relative_absolute_error: 0.3888, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2882, root_relative_squared_error: 0.8687, scimark_benchmark: 2009.1101,
Jason 11 years ago
ran flow weka.LogitBoost_DecisionStump(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.925, f_measure: 0.8053, kappa: 0.744, kb_relative_information_score: 134.9032, mean_absolute_error: 0.0964, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.8127, predictive_accuracy: 0.8049, prior_entropy: 2.3268, recall: 0.8049, relative_absolute_error: 0.4366, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2158, root_relative_squared_error: 0.6503, scimark_benchmark: 2012.2834,
Jason 11 years ago
ran flow weka.RandomForest(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9501, f_measure: 0.8292, kappa: 0.7775, kb_relative_information_score: 140.9584, mean_absolute_error: 0.0914, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.8296, predictive_accuracy: 0.8293, prior_entropy: 2.3268, recall: 0.8293, relative_absolute_error: 0.4136, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.1986, root_relative_squared_error: 0.5985, scimark_benchmark: 2006.0831,
Jason 11 years ago
ran flow weka.SMO_PolyKernel(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.877, f_measure: 0.685, kappa: 0.5992, kb_relative_information_score: 36.433, mean_absolute_error: 0.2103, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.6886, predictive_accuracy: 0.6927, prior_entropy: 2.3268, recall: 0.6927, relative_absolute_error: 0.9517, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.311, root_relative_squared_error: 0.9374, scimark_benchmark: 1899.4398,
Jason 11 years ago
ran flow weka.BayesNet_K2(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8895, f_measure: 0.6528, kappa: 0.56, kb_relative_information_score: 125.6207, mean_absolute_error: 0.0953, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.6557, predictive_accuracy: 0.6585, prior_entropy: 2.3268, recall: 0.6585, relative_absolute_error: 0.4312, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.282, root_relative_squared_error: 0.8499, scimark_benchmark: 2003.216,
Jason 11 years ago
ran flow weka.JRip(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8486, f_measure: 0.7453, kappa: 0.6653, kb_relative_information_score: 132.7814, mean_absolute_error: 0.0901, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.7543, predictive_accuracy: 0.7463, prior_entropy: 2.3268, recall: 0.7463, relative_absolute_error: 0.4079, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2513, root_relative_squared_error: 0.7575, scimark_benchmark: 2019.0099,
Jason 11 years ago
ran flow weka.Bagging_REPTree(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8517, f_measure: 0.6003, kappa: 0.488, kb_relative_information_score: 101.5882, mean_absolute_error: 0.1377, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.5937, predictive_accuracy: 0.6098, prior_entropy: 2.3268, recall: 0.6098, relative_absolute_error: 0.6232, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2712, root_relative_squared_error: 0.8173, scimark_benchmark: 2009.9197,
Jason 11 years ago
ran flow weka.RandomTree(1) on task Supervised Classification on data set autos
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8696, f_measure: 0.7653, kappa: 0.694, kb_relative_information_score: 143.6269, mean_absolute_error: 0.0682, mean_prior_absolute_error: 0.2209, number_of_instances: 205, precision: 0.7734, predictive_accuracy: 0.7659, prior_entropy: 2.3268, recall: 0.7659, relative_absolute_error: 0.3086, root_mean_prior_squared_error: 0.3318, root_mean_squared_error: 0.2452, root_relative_squared_error: 0.7392, scimark_benchmark: 1996.8156,

Metric:

Timeline

Plotting contribution timeline

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

In supervised classification, you are given an input dataset in which instances are labeled with a certain class. The goal is to build a model that predicts the class for future unlabeled instances. The model is evaluated using a train-test procedure, e.g. cross-validation.

To make results by different users comparable, you are given the exact train-test folds to be used, and you need to return at least the predictions generated by your model for each of the test instances. OpenML will use these predictions to calculate a range of evaluation measures on the server.

You can also upload your own evaluation measures, provided that the code for doing so is available from the implementation used. For extremely large datasets, it may be infeasible to upload all predictions. In those cases, you need to compute and provide the evaluations yourself.

Optionally, you can upload the model trained on all the input data. There is no restriction on the file format, but please use a well-known format or PMML.

Given inputs

estimation_procedure 10-fold Crossvalidation
evaluation_measures predictive_accuracy
source_data autos (1)
target_feature symboling

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)

How to submit runs

Using your favorite machine learning environment

Download this task directly in your environment and automatically upload your results

OpenML bootcamp

From your own software

Use one of our APIs to download data from OpenML and upload your results

OpenML APIs