Run
10453845

Run 10453845

Task 9979 (Supervised Classification) cardiotocography Uploaded 18-05-2020 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.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.pipeline.Pipeline(step_0=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbosefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_alpha30.194333424847347
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)_epsilon29.812240610254918
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_ratio0.3432659916931609
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"modified_huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter960
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change2
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_t0.3744581963930176
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.031906997757296336
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

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.

16 Evaluation measures

Area under ROC curve
0.4979 ± 0.006
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0016 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0645 ± 0.0097
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1697 ± 0.0029
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1679 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.1562 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Number of instances
2126
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.1562 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.9134 ± 0.0053
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0108 ± 0.0171
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2897 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.406 ± 0.0164
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.4013 ± 0.0567
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.0991 ± 0.0022
Cross-validation details (10-fold Crossvalidation)