Run
10459376

Run 10459376

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.discriminant_analysis.LinearDiscriminantAnalysis),step_1=skle arn.linear_model._stochastic_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.linear_model._stochastic_gradient.SGDClassifier(2)_alpha0.0002809481623037133
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_averagetrue
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)_epsilon0.48328695450711595
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.7710182527357426
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.2832968855587405
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"constant"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"hinge"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter393
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"l1"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.1858426991480957
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.682564703712653
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.9066935128758408
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components354
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.6137878855330663
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"lsqr"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.14114152399787175
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),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.discriminant_analysis.LinearDiscriminantAnalysis),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
0.9609 ± 0.0163
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9313 ± 0.0281
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9219 ± 0.0327
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9268 ± 0.0306
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0154 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9306 ± 0.029
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9332 ± 0.0232
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9306 ± 0.029
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0781 ± 0.0327
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1242 ± 0.0262
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3953 ± 0.0833
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9306 ± 0.029
Cross-validation details (10-fold Crossvalidation)