Run
10462318

Run 10462318

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=sklearn.feature_selection._univariate_sele ction.SelectPercentile,step_1=sklearn.linear_model._stochastic_gradient.SGD Classifier)(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)_alpha6.745173895341542e-07
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)_epsilon37.557208928212134
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.43247510932418065
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.5159512561035384
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"constant"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"modified_huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1678
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change59
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_t0.499848410271937
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.34173238137174133
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.feature_selection._univariate_selection.SelectPercentile(1)_percentile91.54455285578692
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.chi2"}
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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.feature_selection._univariate_selection.SelectPercentile,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 ± 0.0239
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8062 ± 0.0238
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6122 ± 0.0476
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6123 ± 0.0475
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1938 ± 0.0237
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8062 ± 0.0237
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8063 ± 0.0238
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8062 ± 0.0237
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3877 ± 0.0475
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4402 ± 0.0268
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8805 ± 0.0537
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.806 ± 0.0239
Cross-validation details (10-fold Crossvalidation)