Run
10457955

Run 10457955

Task 9981 (Supervised Classification) cnae-9 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=sklearn.preprocessing._data.StandardScaler ,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.preprocessing._data.StandardScaler(1)_copyfalse
sklearn.preprocessing._data.StandardScaler(1)_with_meantrue
sklearn.preprocessing._data.StandardScaler(1)_with_stdfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_alpha0.0008002838241436994
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)_epsilon4.67833758729079
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.2477006475242767
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.042811959041985335
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"constant"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"squared_hinge"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter38
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"elasticnet"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.7806899067063496
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.3885375499601775
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.9311575246953921
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,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.preprocessing._data.StandardScaler,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
0.8755 ± 0.0194
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7753 ± 0.0367
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.751 ± 0.0388
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.7668 ± 0.0363
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0492 ± 0.0077
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7787 ± 0.0345
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7785 ± 0.0445
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7787 ± 0.0345
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.249 ± 0.0388
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2218 ± 0.0175
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7056 ± 0.0556
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7787 ± 0.0345
Cross-validation details (10-fold Crossvalidation)