Run
10464552

Run 10464552

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 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.cluster._agglomerative.FeatureAggl omeration,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)_alpha9.286952327116387
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)_epsilon41.18075188706478
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.8704070093448559
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.020395091959157563
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"squared_loss"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1480
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change52
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"elasticnet"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.6045010219137877
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.48951237956912635
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.49903592018262877
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_affinity"precomputed"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_compute_full_treefalse
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_connectivitynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_distance_thresholdnull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_linkage"complete"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memorynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters9
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func{"oml-python:serialized_object": "function", "value": "numpy.median"}
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,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.cluster._agglomerative.FeatureAgglomeration,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

0.4999 ± 0.0002
Per class
Cross-validation details (10-fold Crossvalidation)
0.4502
Per class
Cross-validation details (10-fold Crossvalidation)
-0.0002 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.0677 ± 0.0658
Cross-validation details (10-fold Crossvalidation)
0.4591 ± 0.0324
Cross-validation details (10-fold Crossvalidation)
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5409 ± 0.0324
Cross-validation details (10-fold Crossvalidation)
14980
Per class
Cross-validation details (10-fold Crossvalidation)
0.505
Per class
Cross-validation details (10-fold Crossvalidation)
0.5409 ± 0.0324
Cross-validation details (10-fold Crossvalidation)
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.928 ± 0.0655
Cross-validation details (10-fold Crossvalidation)
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
0.6776 ± 0.0229
Cross-validation details (10-fold Crossvalidation)
1.3624 ± 0.0462
Cross-validation details (10-fold Crossvalidation)
0.4999 ± 0.0002
Cross-validation details (10-fold Crossvalidation)