Run
10460892

Run 10460892

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.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEs timator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_2=s klearn.naive_bayes.BernoulliNB)(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.ensemble._forest.RandomForestClassifier(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.5958966218059125
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth12
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features5
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes1208
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.30073599128228057
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.30950962617658007
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.4843368090273703
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.31510271897494013
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.2501174994621978
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1020
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.naive_bayes.BernoulliNB(11)_alpha42.15184286519816
sklearn.naive_bayes.BernoulliNB(11)_binarize0.0
sklearn.naive_bayes.BernoulliNB(11)_class_priornull
sklearn.naive_bayes.BernoulliNB(11)_fit_priortrue
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_2=sklearn.naive_bayes.BernoulliNB)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_2=sklearn.naive_bayes.BernoulliNB)(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.ensemble._forest.RandomForestClassifier),step_2=sklearn.naive_bayes.BernoulliNB)(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.

18 Evaluation measures

Area under ROC curve
0.6626 ± 0.0407
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8294 ± 0.0104
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0213 ± 0.0535
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.1394 ± 0.0889
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1746 ± 0.009
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2041 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8691 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
Number of instances
4521
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8042 ± 0.0215
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8691 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5155 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8558 ± 0.0435
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3193 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3268 ± 0.0123
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0235 ± 0.0383
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.507 ± 0.0175
Cross-validation details (10-fold Crossvalidation)