Run
10460313

Run 10460313

Task 9899 (Supervised Classification) bank-marketing Uploaded 20-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEs timator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_2=skle arn.svm._classes.SVC)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.tree._classes.DecisionTreeClassifier(3)_ccp_alpha0.5079700238063408
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth33
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features0.3337125863228991
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes1653
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease0.10370985537730193
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf0.012573020678285316
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split0.3211780173353695
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf0.13201944852269298
sklearn.tree._classes.DecisionTreeClassifier(3)_presort"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter"best"
sklearn.svm._classes.SVC(4)_C0.009835776746118493
sklearn.svm._classes.SVC(4)_break_tiesfalse
sklearn.svm._classes.SVC(4)_cache_size200
sklearn.svm._classes.SVC(4)_class_weightnull
sklearn.svm._classes.SVC(4)_coef08793.1747208658
sklearn.svm._classes.SVC(4)_decision_function_shape"ovo"
sklearn.svm._classes.SVC(4)_degree3
sklearn.svm._classes.SVC(4)_gamma11999.678407101084
sklearn.svm._classes.SVC(4)_kernel"sigmoid"
sklearn.svm._classes.SVC(4)_max_iter-1
sklearn.svm._classes.SVC(4)_probabilitytrue
sklearn.svm._classes.SVC(4)_random_state42
sklearn.svm._classes.SVC(4)_shrinkingtrue
sklearn.svm._classes.SVC(4)_tol1.57661416971023e-05
sklearn.svm._classes.SVC(4)_verbosefalse
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_2=sklearn.svm._classes.SVC)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_2=sklearn.svm._classes.SVC)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_2=sklearn.svm._classes.SVC)(1)_verbosefalse

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.

16 Evaluation measures

0.5067 ± 0.0599
Per class
Cross-validation details (10-fold Crossvalidation)
-0.0156 ± 0.0196
Cross-validation details (10-fold Crossvalidation)
0.2037 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.2041 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
0.8848 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
4521
Per class
Cross-validation details (10-fold Crossvalidation)
0.8848 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.5155 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
0.9984 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.3193 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.3195 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
1.0004 ± 0.0016
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)