Run
10458594

Run 10458594

Task 9981 (Supervised Classification) cnae-9 Uploaded 19-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • automl_meta_features openml-python Sklearn_0.22.1.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,ste p_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)_alpha0.0009257876770808262
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)_epsilon32.96440506820473
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.3157083216660251
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"modified_huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1840
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change22
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_t0.5418149258865353
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.43896044063078865
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.6566174394069657
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.preprocessing._data.Normalizer(1)_copyfalse
sklearn.preprocessing._data.Normalizer(1)_norm"l1"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,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.Normalizer,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
0.9891 ± 0.0034
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9131 ± 0.0251
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.901 ± 0.0287
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.7336 ± 0.0153
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0794 ± 0.004
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.912 ± 0.0255
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9158 ± 0.0224
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.912 ± 0.0255
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.402 ± 0.0203
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1707 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5432 ± 0.0184
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.912 ± 0.0255
Cross-validation details (10-fold Crossvalidation)