Run
10462806

Run 10462806

Task 3891 (Supervised Classification) gina_agnostic Uploaded 21-05-2020 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)_alpha0.9848126738161395
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)_epsilon48.600008823093525
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.265405094985558
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.0723865736621707
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"squared_hinge"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1507
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change31
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_t0.36501709972749585
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.22574406950230064
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.9169851033570179
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components56
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.9096036712142489
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"lsqr"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol8.173352031879909e-06
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
0.806 ± 0.0153
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8063 ± 0.0153
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6126 ± 0.0305
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6129 ± 0.0305
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1935 ± 0.0152
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8065 ± 0.0152
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8072 ± 0.0151
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8065 ± 0.0152
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3871 ± 0.0305
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4399 ± 0.0179
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8799 ± 0.0358
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.806 ± 0.0153
Cross-validation details (10-fold Crossvalidation)