Run
10458647

Run 10458647

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.impute._knn.KNNImputer,step_1=skle arn.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)_alpha7.979522884757161e-05
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)_epsilon9.480716258493228
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.718643855991015
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.2918869401737511
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_iter1972
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change76
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_t0.5612947816588102
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.13131154496614802
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.6665533086921888
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.impute._knn.KNNImputer(1)_add_indicatorfalse
sklearn.impute._knn.KNNImputer(1)_copyfalse
sklearn.impute._knn.KNNImputer(1)_metric"nan_euclidean"
sklearn.impute._knn.KNNImputer(1)_missing_valuesNaN
sklearn.impute._knn.KNNImputer(1)_n_neighbors8
sklearn.impute._knn.KNNImputer(1)_weights"distance"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,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.impute._knn.KNNImputer,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
0.9177 ± 0.0147
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8562 ± 0.0244
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8354 ± 0.0294
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8459 ± 0.0275
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0325 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8537 ± 0.0261
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8707 ± 0.0227
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8537 ± 0.0261
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1646 ± 0.0294
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1803 ± 0.0174
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5737 ± 0.0554
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8537 ± 0.0261
Cross-validation details (10-fold Crossvalidation)