Run
10462248

Run 10462248

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=automl.components.data_preprocessing.imput ation.ImputationComponent,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)_alpha0.72210286308653
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)_epsilon20.777828081326323
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_ratio0.6042632362914638
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"squared_epsilon_insensitive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter602
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change90
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_t0.23038515359256587
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.5893317036505439
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.2287951072169161
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,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=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbosefalse
automl.components.data_preprocessing.imputation.ImputationComponent(1)_add_indicatorfalse
automl.components.data_preprocessing.imputation.ImputationComponent(1)_missing_valuesNaN
automl.components.data_preprocessing.imputation.ImputationComponent(1)_strategy"mean"

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
0.5
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0163 ± 0.0026
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4916 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5084 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5084 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9836 ± 0.0026
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.7012 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.4025 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)