Run
10456519

Run 10456519

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 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.naive_bayes.BernoulliNB),step_1=sklearn.linear_model._stochas tic_gradient.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.naive_bayes.BernoulliNB(11)_alpha71.13799026923637
sklearn.naive_bayes.BernoulliNB(11)_binarize0.0
sklearn.naive_bayes.BernoulliNB(11)_class_priornull
sklearn.naive_bayes.BernoulliNB(11)_fit_priorfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_alpha1.8644397434557058
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_stoppingtrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon7.575154122164567
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.11266449170195912
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"squared_loss"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1728
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change49
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"l1"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.8933208774561762
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.917419440702024
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.19725083820007194
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=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"}}]
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=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
0.7576 ± 0.1758
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7478 ± 0.0916
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1319 ± 0.0809
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-3.8974 ± 1.5502
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3459 ± 0.1089
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6541 ± 0.1089
Cross-validation details (10-fold Crossvalidation)
Number of instances
4839
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9421 ± 0.036
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6541 ± 0.1089
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3029 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
3.3843 ± 1.0689
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5882 ± 0.0809
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
2.6037 ± 0.3606
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7576 ± 0.1758
Cross-validation details (10-fold Crossvalidation)