Run
10462252

Run 10462252

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=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)_alpha0.6249852223938563
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_stoppingfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon46.905462718716855
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.4152544802869711
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.23766219661157653
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter997
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change11
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_t0.3658237885530675
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.1398263788677137
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_affinity"l2"
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"single"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memorynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters688
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func{"oml-python:serialized_object": "function", "value": "numpy.mean"}
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

Area under ROC curve
0.4894 ± 0.0398
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4882 ± 0.0441
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0213 ± 0.0798
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0194 ± 0.0789
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.5095 ± 0.0395
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.4905 ± 0.0395
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4893 ± 0.0432
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4905 ± 0.0395
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0193 ± 0.0789
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.7138 ± 0.0285
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.4278 ± 0.0571
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4894 ± 0.0398
Cross-validation details (10-fold Crossvalidation)