Run
10463592

Run 10463592

Task 3021 (Supervised Classification) sick 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.impute._base.MissingIndicator,step _1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.linear_mod el._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)_alpha0.3400008665998555
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)_epsilon36.88767198335339
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.5507051095380117
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.601612474058282
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"log"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1565
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change78
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_t0.10550471544763809
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.08659291203001446
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.9289256008998784
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.preprocessing._data.PolynomialFeatures(1)_degree2
sklearn.preprocessing._data.PolynomialFeatures(1)_include_biastrue
sklearn.preprocessing._data.PolynomialFeatures(1)_interaction_onlyfalse
sklearn.preprocessing._data.PolynomialFeatures(1)_order"C"
sklearn.impute._base.MissingIndicator(1)_error_on_newtrue
sklearn.impute._base.MissingIndicator(1)_features"all"
sklearn.impute._base.MissingIndicator(1)_missing_valuesNaN
sklearn.impute._base.MissingIndicator(1)_sparse"auto"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=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
0.3854 ± 0.0314
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7553 ± 0.0267
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0883 ± 0.0224
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-3.2077 ± 0.4725
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.361 ± 0.0367
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1152 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6723 ± 0.0393
Cross-validation details (10-fold Crossvalidation)
Number of instances
3772
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8649 ± 0.0072
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6723 ± 0.0393
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3324 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
3.1342 ± 0.3173
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2398 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5654 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
2.3582 ± 0.1397
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.3844 ± 0.0353
Cross-validation details (10-fold Crossvalidation)