Run
10458183

Run 10458183

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.QuantileTransf ormer,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)_alpha0.4033274522513182
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)_epsilon29.95406993512866
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.3725750497253937
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.29764760664318557
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"constant"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"perceptron"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter92
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change49
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_t0.09785754960935521
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.73208232613747
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.preprocessing._data.QuantileTransformer(1)_copyfalse
sklearn.preprocessing._data.QuantileTransformer(1)_ignore_implicit_zerosfalse
sklearn.preprocessing._data.QuantileTransformer(1)_n_quantiles959
sklearn.preprocessing._data.QuantileTransformer(1)_output_distribution"normal"
sklearn.preprocessing._data.QuantileTransformer(1)_random_state42
sklearn.preprocessing._data.QuantileTransformer(1)_subsample18922588
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,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.QuantileTransformer,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
0.6474 ± 0.0521
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.3982 ± 0.0833
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2948 ± 0.1043
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3395 ± 0.0977
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1393 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.3731 ± 0.0927
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7163 ± 0.0568
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.3731 ± 0.0927
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7052 ± 0.1043
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3732 ± 0.0281
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1876 ± 0.0894
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.3731 ± 0.0927
Cross-validation details (10-fold Crossvalidation)