Run
10593671

Run 10593671

Task 361444 (Supervised Classification) phoneme Uploaded 09-05-2023 by Takeaki Sakabe
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=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.impute._base.SimpleImputer(43)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(43)_copytrue
sklearn.impute._base.SimpleImputer(43)_fill_valuenull
sklearn.impute._base.SimpleImputer(43)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(43)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(43)_strategy"mean"
sklearn.impute._base.SimpleImputer(43)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbosefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_alpha0.0001
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_averagefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_class_weightnull
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_early_stoppingfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_epsilon0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_eta00.0
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_l1_ratio0.15
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_learning_rate"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_loss"hinge"
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_max_iter1000
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_n_iter_no_change5
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_n_jobsnull
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_penalty"l2"
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_power_t0.5
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_random_state1589
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_tol0.001
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_validation_fraction0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(4)_warm_startfalse

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
0.6826 ± 0.0641
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
F measure
0.748 ± 0.0358
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Kappa
0.381 ± 0.1043
Cross-validation details (5 times 2-fold Crossvalidation)
KB relative information score
0.3603 ± 0.045
Cross-validation details (5 times 2-fold Crossvalidation)
Mean absolute error
0.2461 ± 0.0173
Cross-validation details (5 times 2-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Weighted recall
0.7539 ± 0.0173
Cross-validation details (5 times 2-fold Crossvalidation)
Number of instances
27020
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Precision
0.745 ± 0.0293
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Predictive accuracy
0.7539 ± 0.0173
Cross-validation details (5 times 2-fold Crossvalidation)
Prior entropy
0.8732 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Relative absolute error
0.5934 ± 0.0417
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean prior squared error
0.4554 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean squared error
0.4961 ± 0.0173
Cross-validation details (5 times 2-fold Crossvalidation)
Root relative squared error
1.0895 ± 0.0379
Cross-validation details (5 times 2-fold Crossvalidation)
Unweighted recall
0.6826 ± 0.0641
Cross-validation details (5 times 2-fold Crossvalidation)