Run
10458846

Run 10458846

Task 9981 (Supervised Classification) cnae-9 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=sklearn.preprocessing._discretization.KBin sDiscretizer,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)_alpha0.0001329421178225246
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_stoppingfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon16.03170916513778
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.3907222319000993
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.28487497298468184
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"log"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1560
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change5
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"l1"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.7503398321629273
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.11947559261109941
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._discretization.KBinsDiscretizer(1)_encode"onehot-dense"
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_n_bins95
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_strategy"quantile"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,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._discretization.KBinsDiscretizer,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
0.7704 ± 0.0461
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.3952
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3417 ± 0.154
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2151 ± 0.0472
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1831 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.4148 ± 0.1369
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6282
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4148 ± 0.1369
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9271 ± 0.0219
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3049 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9702 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4148 ± 0.1369
Cross-validation details (10-fold Crossvalidation)