Run
10454916

Run 10454916

Task 3735 (Supervised Classification) pollen 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=sklearn.decomposition._factor_analysis.Fac torAnalysis,step_1=sklearn.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)_alpha5.7443613523485595e-05
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_stoppingfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon45.92165920658907
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.8147523718828766
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.9944903558789698
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"modified_huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter2250
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change22
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_t0.7776405057290693
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.2897380448611037
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
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_copyfalse
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_iterated_power34
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_max_iter4920
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_n_components1
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_noise_variance_initnull
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_random_state42
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_svd_method"randomized"
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_tol4.278847204651663
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,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=sklearn.decomposition._factor_analysis.FactorAnalysis,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
0.4861 ± 0.0276
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4916 ± 0.0234
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0166 ± 0.047
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0003 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.5001 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.5
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.4917 ± 0.0235
Cross-validation details (10-fold Crossvalidation)
Number of instances
3848
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4917 ± 0.0245
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4917 ± 0.0235
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0002 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5001 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0002 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4917 ± 0.0235
Cross-validation details (10-fold Crossvalidation)