Run
10228399

Run 10228399

Task 2079 (Supervised Classification) eucalyptus Uploaded 04-06-2019 by Felix Neutatz
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • ComplexityDriven openml-python Sklearn_0.20.3.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.C376925338660a4(n6=sklearn.pipeline.C3769253386595a(n7=skl earn.pipeline.C37692533864a62(n8=sklearn.compose._column_transformer.C58a83 b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)),n 10=sklearn.pipeline.C376925338655aa(n11=sklearn.compose._column_transformer .C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338 651f9))),n13=fastsklearnfeature.transformations.HigherOrderCommutativeTrans formation.C37692533865ef0,c=sklearn.linear_model.logistic.LogisticRegressio n)(1)Automatically created scikit-learn flow.
sklearn.linear_model.logistic.LogisticRegression(23)_C0.1
sklearn.linear_model.logistic.LogisticRegression(23)_class_weight"balanced"
sklearn.linear_model.logistic.LogisticRegression(23)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(23)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(23)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(23)_max_iter10000
sklearn.linear_model.logistic.LogisticRegression(23)_multi_class"auto"
sklearn.linear_model.logistic.LogisticRegression(23)_n_jobsnull
sklearn.linear_model.logistic.LogisticRegression(23)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(23)_random_state21205
sklearn.linear_model.logistic.LogisticRegression(23)_solver"lbfgs"
sklearn.linear_model.logistic.LogisticRegression(23)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(23)_verbose0
sklearn.linear_model.logistic.LogisticRegression(23)_warm_startfalse
sklearn.pipeline.C376925338660a4(n6=sklearn.pipeline.C3769253386595a(n7=sklearn.pipeline.C37692533864a62(n8=sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)),n10=sklearn.pipeline.C376925338655aa(n11=sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9))),n13=fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C37692533865ef0,c=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.C376925338660a4(n6=sklearn.pipeline.C3769253386595a(n7=sklearn.pipeline.C37692533864a62(n8=sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)),n10=sklearn.pipeline.C376925338655aa(n11=sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9))),n13=fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C37692533865ef0,c=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n6", "step_name": "n6"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n13", "step_name": "n13"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "c", "step_name": "c"}}]
sklearn.pipeline.C3769253386595a(n7=sklearn.pipeline.C37692533864a62(n8=sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)),n10=sklearn.pipeline.C376925338655aa(n11=sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9)))(1)_n_jobsnull
sklearn.pipeline.C3769253386595a(n7=sklearn.pipeline.C37692533864a62(n8=sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)),n10=sklearn.pipeline.C376925338655aa(n11=sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9)))(1)_transformer_list[{"oml-python:serialized_object": "component_reference", "value": {"key": "n7", "step_name": "n7"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n10", "step_name": "n10"}}]
sklearn.pipeline.C3769253386595a(n7=sklearn.pipeline.C37692533864a62(n8=sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)),n10=sklearn.pipeline.C376925338655aa(n11=sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9)))(1)_transformer_weightsnull
sklearn.pipeline.C37692533864a62(n8=sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4))(1)_memorynull
sklearn.pipeline.C37692533864a62(n8=sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n8", "step_name": "n8"}}]
sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)(1)_n_jobsnull
sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)(1)_remainder"drop"
sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C58a83b85a3a49(n9=sklearn.preprocessing._function_transformer.C376925338643d4)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n9", "step_name": "n9", "argument_1": [17]}}]
sklearn.preprocessing._function_transformer.C376925338643d4(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C376925338643d4(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C376925338643d4(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C376925338643d4(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C376925338643d4(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C376925338643d4(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C376925338643d4(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C376925338643d4(1)_validatefalse
sklearn.pipeline.C376925338655aa(n11=sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9))(1)_memorynull
sklearn.pipeline.C376925338655aa(n11=sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n11", "step_name": "n11"}}]
sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9)(1)_n_jobsnull
sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9)(1)_remainder"drop"
sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C37692533865379(n12=sklearn.preprocessing._function_transformer.C376925338651f9)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n12", "step_name": "n12", "argument_1": [14]}}]
sklearn.preprocessing._function_transformer.C376925338651f9(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C376925338651f9(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C376925338651f9(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C376925338651f9(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C376925338651f9(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C376925338651f9(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C376925338651f9(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C376925338651f9(1)_validatefalse
fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C37692533865ef0(1)_method{"oml-python:serialized_object": "function", "value": "numpy.nanprod"}
fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C37692533865ef0(1)_number_parent_features2
fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C37692533865ef0(1)_sympy_method0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

17 Evaluation measures

Area under ROC curve
0.8643 ± 0.0172
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5658 ± 0.0343
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4555 ± 0.0434
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.4472 ± 0.0139
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2161 ± 0.0044
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3132 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
736
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5852 ± 0.0359
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5652 ± 0.0348
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.2621 ± 0.0054
Cross-validation details (10-fold Crossvalidation)
Recall
0.5652 ± 0.0348
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6898 ± 0.0139
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3957 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3254 ± 0.0044
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8224 ± 0.011
Cross-validation details (10-fold Crossvalidation)