Run
10228278

Run 10228278

Task 10103 (Supervised Classification) volcanoes-a1 Uploaded 01-06-2019 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.C3766a0b2aa4e23(n5=sklearn.pipeline.C58a434511074f(n6=skle arn.pipeline.C3766a0b2aa3e04(n7=sklearn.compose._column_transformer.C3766a0 b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)),n 9=sklearn.pipeline.C3766a0b2aa46ae(n10=sklearn.compose._column_transformer. C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa 4461))),n12=fastsklearnfeature.transformations.HigherOrderCommutativeTransf ormation.C3766a0b2aa4cc0,c=sklearn.linear_model.logistic.LogisticRegression )(1)Automatically created scikit-learn flow.
sklearn.linear_model.logistic.LogisticRegression(23)_C0.001
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_state51292
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.C3766a0b2aa4e23(n5=sklearn.pipeline.C58a434511074f(n6=sklearn.pipeline.C3766a0b2aa3e04(n7=sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)),n9=sklearn.pipeline.C3766a0b2aa46ae(n10=sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461))),n12=fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C3766a0b2aa4cc0,c=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.C3766a0b2aa4e23(n5=sklearn.pipeline.C58a434511074f(n6=sklearn.pipeline.C3766a0b2aa3e04(n7=sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)),n9=sklearn.pipeline.C3766a0b2aa46ae(n10=sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461))),n12=fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C3766a0b2aa4cc0,c=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n5", "step_name": "n5"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n12", "step_name": "n12"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "c", "step_name": "c"}}]
sklearn.pipeline.C58a434511074f(n6=sklearn.pipeline.C3766a0b2aa3e04(n7=sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)),n9=sklearn.pipeline.C3766a0b2aa46ae(n10=sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461)))(1)_n_jobsnull
sklearn.pipeline.C58a434511074f(n6=sklearn.pipeline.C3766a0b2aa3e04(n7=sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)),n9=sklearn.pipeline.C3766a0b2aa46ae(n10=sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461)))(1)_transformer_list[{"oml-python:serialized_object": "component_reference", "value": {"key": "n6", "step_name": "n6"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n9", "step_name": "n9"}}]
sklearn.pipeline.C58a434511074f(n6=sklearn.pipeline.C3766a0b2aa3e04(n7=sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)),n9=sklearn.pipeline.C3766a0b2aa46ae(n10=sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461)))(1)_transformer_weightsnull
sklearn.pipeline.C3766a0b2aa3e04(n7=sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938))(1)_memorynull
sklearn.pipeline.C3766a0b2aa3e04(n7=sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n7", "step_name": "n7"}}]
sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)(1)_n_jobsnull
sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)(1)_remainder"drop"
sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C3766a0b2aa3bca(n8=sklearn.preprocessing._function_transformer.C3766a0b2aa3938)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n8", "step_name": "n8", "argument_1": [2]}}]
sklearn.preprocessing._function_transformer.C3766a0b2aa3938(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C3766a0b2aa3938(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C3766a0b2aa3938(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C3766a0b2aa3938(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C3766a0b2aa3938(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C3766a0b2aa3938(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C3766a0b2aa3938(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C3766a0b2aa3938(1)_validatefalse
sklearn.pipeline.C3766a0b2aa46ae(n10=sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461))(1)_memorynull
sklearn.pipeline.C3766a0b2aa46ae(n10=sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n10", "step_name": "n10"}}]
sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461)(1)_n_jobsnull
sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461)(1)_remainder"drop"
sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C3766a0b2aa4565(n11=sklearn.preprocessing._function_transformer.C3766a0b2aa4461)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n11", "step_name": "n11", "argument_1": [2]}}]
sklearn.preprocessing._function_transformer.C3766a0b2aa4461(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C3766a0b2aa4461(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C3766a0b2aa4461(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C3766a0b2aa4461(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C3766a0b2aa4461(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C3766a0b2aa4461(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C3766a0b2aa4461(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C3766a0b2aa4461(1)_validatefalse
fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C3766a0b2aa4cc0(1)_method{"oml-python:serialized_object": "function", "value": "numpy.nanprod"}
fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C3766a0b2aa4cc0(1)_number_parent_features2
fastsklearnfeature.transformations.HigherOrderCommutativeTransformation.C3766a0b2aa4cc0(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

0.94 ± 0.0201
Per class
Cross-validation details (10-fold Crossvalidation)
0.8898
Per class
0.3828 ± 0.0416
Cross-validation details (10-fold Crossvalidation)
-12985.2527 ± 4.8061
Cross-validation details (10-fold Crossvalidation)
0.319 ± 0
Cross-validation details (10-fold Crossvalidation)
0.0699 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
3252
Per class
Cross-validation details (10-fold Crossvalidation)
0.8816
Per class
0.901 ± 0.006
Cross-validation details (10-fold Crossvalidation)
0.6323
Cross-validation details (10-fold Crossvalidation)
0.901 ± 0.006
Per class
Cross-validation details (10-fold Crossvalidation)
4.5632 ± 0.0449
Cross-validation details (10-fold Crossvalidation)
0.1864 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
0.3987 ± 0
Cross-validation details (10-fold Crossvalidation)
2.1387 ± 0.0212
Cross-validation details (10-fold Crossvalidation)