Run
10461231

Run 10461231

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.mu lti_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.de composition._fastica.FastICA,step_2=sklearn.linear_model._stochastic_gradie nt.SGDClassifier)(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.linear_model._stochastic_gradient.SGDClassifier(2)_alpha1.5365534507672069e-07
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_averagefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_class_weightnull
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_early_stoppingfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon243776.4008546401
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.2913187795554579
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.1661466318480088
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter696202798
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change20
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"l2"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t3.5675919172937154
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.48201050348996993
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.decomposition._fastica.FastICA(1)_algorithm"parallel"
sklearn.decomposition._fastica.FastICA(1)_fun"exp"
sklearn.decomposition._fastica.FastICA(1)_fun_argsnull
sklearn.decomposition._fastica.FastICA(1)_max_iter93
sklearn.decomposition._fastica.FastICA(1)_n_components3
sklearn.decomposition._fastica.FastICA(1)_random_state42
sklearn.decomposition._fastica.FastICA(1)_tol0.31365845859895214
sklearn.decomposition._fastica.FastICA(1)_w_initnull
sklearn.decomposition._fastica.FastICA(1)_whitentrue
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,step_2=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,step_2=sklearn.linear_model._stochastic_gradient.SGDClassifier)(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.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,step_2=sklearn.linear_model._stochastic_gradient.SGDClassifier)(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
0.7421 ± 0.0377
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7606 ± 0.0161
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2535 ± 0.0404
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.8574 ± 0.1261
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2909 ± 0.0199
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2041 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7091 ± 0.0199
Cross-validation details (10-fold Crossvalidation)
Number of instances
4521
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8799 ± 0.0133
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7091 ± 0.0199
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5155 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.4255 ± 0.097
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
0.5393 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.689 ± 0.0576
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7421 ± 0.0377
Cross-validation details (10-fold Crossvalidation)