Run
10457709

Run 10457709

Task 3797 (Supervised Classification) socmob 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=automl.components.feature_preprocessing.mu lti_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.pr eprocessing._data.PolynomialFeatures,step_2=sklearn.linear_model._stochasti c_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)_alpha4.4874707852939035e-05
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_averagefalse
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)_epsilon8.157671778946558
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.6853438427664468
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.3731766156839747
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"log"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter274
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change26
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_t0.8878910736513713
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.5445374280694547
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.7823553478819376
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.preprocessing._data.PolynomialFeatures(1)_degree4
sklearn.preprocessing._data.PolynomialFeatures(1)_include_biastrue
sklearn.preprocessing._data.PolynomialFeatures(1)_interaction_onlyfalse
sklearn.preprocessing._data.PolynomialFeatures(1)_order"C"
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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
0.8124 ± 0.0273
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7585 ± 0.0622
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4495 ± 0.0938
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1236 ± 0.2289
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2638 ± 0.0702
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7362 ± 0.0702
Cross-validation details (10-fold Crossvalidation)
Number of instances
1156
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8628 ± 0.0128
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7362 ± 0.0702
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7646 ± 0.2009
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4152 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5137 ± 0.0762
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2371 ± 0.1794
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8124 ± 0.0273
Cross-validation details (10-fold Crossvalidation)