Run
10315571

Run 10315571

Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 13-08-2019 by Felix Neutatz
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  • ComplexityDriven openml-python Sklearn_0.20.3.
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Flow

sklearn.pipeline.C379fc537f3af4a(n3=sklearn.compose._column_transformer.C37 9fc537f3a8b7(n4=sklearn.preprocessing._function_transformer.C379fc537f3a6d5 ),n5=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizer Transformation.C379fc537f3ac67,c=sklearn.linear_model.logistic.LogisticRegr ession)(1)Automatically created scikit-learn flow.
sklearn.linear_model.logistic.LogisticRegression(28)_C0.001
sklearn.linear_model.logistic.LogisticRegression(28)_class_weight"balanced"
sklearn.linear_model.logistic.LogisticRegression(28)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(28)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(28)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(28)_max_iter10000
sklearn.linear_model.logistic.LogisticRegression(28)_multi_class"auto"
sklearn.linear_model.logistic.LogisticRegression(28)_n_jobsnull
sklearn.linear_model.logistic.LogisticRegression(28)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(28)_random_state60551
sklearn.linear_model.logistic.LogisticRegression(28)_solver"lbfgs"
sklearn.linear_model.logistic.LogisticRegression(28)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(28)_verbose0
sklearn.linear_model.logistic.LogisticRegression(28)_warm_startfalse
sklearn.pipeline.C379fc537f3af4a(n3=sklearn.compose._column_transformer.C379fc537f3a8b7(n4=sklearn.preprocessing._function_transformer.C379fc537f3a6d5),n5=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizerTransformation.C379fc537f3ac67,c=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.C379fc537f3af4a(n3=sklearn.compose._column_transformer.C379fc537f3a8b7(n4=sklearn.preprocessing._function_transformer.C379fc537f3a6d5),n5=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizerTransformation.C379fc537f3ac67,c=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n3", "step_name": "n3"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n5", "step_name": "n5"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "c", "step_name": "c"}}]
sklearn.compose._column_transformer.C379fc537f3a8b7(n4=sklearn.preprocessing._function_transformer.C379fc537f3a6d5)(1)_n_jobsnull
sklearn.compose._column_transformer.C379fc537f3a8b7(n4=sklearn.preprocessing._function_transformer.C379fc537f3a6d5)(1)_remainder"drop"
sklearn.compose._column_transformer.C379fc537f3a8b7(n4=sklearn.preprocessing._function_transformer.C379fc537f3a6d5)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C379fc537f3a8b7(n4=sklearn.preprocessing._function_transformer.C379fc537f3a6d5)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C379fc537f3a8b7(n4=sklearn.preprocessing._function_transformer.C379fc537f3a6d5)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n4", "step_name": "n4", "argument_1": [0]}}]
sklearn.preprocessing._function_transformer.C379fc537f3a6d5(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C379fc537f3a6d5(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C379fc537f3a6d5(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C379fc537f3a6d5(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C379fc537f3a6d5(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C379fc537f3a6d5(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C379fc537f3a6d5(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C379fc537f3a6d5(1)_validatefalse

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
0.6594 ± 0.0458
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6645 ± 0.0381
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2682 ± 0.0676
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.6963 ± 0.0177
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4973 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.363 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Number of instances
748
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7689 ± 0.0328
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6377 ± 0.0399
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7916 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
Recall
0.6377 ± 0.0399
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.3698 ± 0.0082
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4258 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4974 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1679 ± 0.0071
Cross-validation details (10-fold Crossvalidation)