Run
10461996

Run 10461996

Task 3891 (Supervised Classification) gina_agnostic Uploaded 20-05-2020 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)_alpha0.00011565666388838428
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)_epsilon24.534562210215256
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.022171652736213705
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_iter2128
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change3
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_t0.001230611460066966
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.8493677532299138
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.5099805587553118
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.

18 Evaluation measures

Area under ROC curve
0.7804 ± 0.0253
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7805 ± 0.0252
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.5609 ± 0.0505
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.561 ± 0.0505
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2194 ± 0.0252
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7806 ± 0.0252
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7806 ± 0.0255
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7806 ± 0.0252
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.439 ± 0.0504
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4684 ± 0.0269
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.937 ± 0.0538
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7804 ± 0.0253
Cross-validation details (10-fold Crossvalidation)