Run
10459338

Run 10459338

Task 9981 (Supervised Classification) cnae-9 Uploaded 20-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(esti mator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.ensemble ._forest.RandomForestClassifier)(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_alpha0.6476516685393157
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth153
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features0.9707884467624054
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes1010
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease0.012709791156647988
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf0.1446359977016164
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split0.3370953169728455
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf0.3281141446333447
sklearn.tree._classes.DecisionTreeClassifier(3)_presort"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter"best"
sklearn.ensemble._forest.RandomForestClassifier(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.2126066497352046
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth673
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features1
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes635
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.04106250474655677
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.14050434418029234
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.2940098087573797
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.07996663262937971
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.03940245289581207
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators831
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.ensemble._forest.RandomForestClassifier)(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"}}]
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.ensemble._forest.RandomForestClassifier)(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

Area under ROC curve
0.6329 ± 0.0273
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0771 ± 0.0209
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0003 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1975 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.1796 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.1796 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3143 ± 0
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9999 ± 0
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.1796 ± 0.0186
Cross-validation details (10-fold Crossvalidation)