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10588980

Run 10588980

Task 146065 (Supervised Classification) monks-problems-2 Uploaded 27-09-2022 by VAIBHAV JAISWAL
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Flow

sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=skl earn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.One HotEncoder),model=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(30)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(30)_copytrue
sklearn.impute._base.SimpleImputer(30)_fill_value-1
sklearn.impute._base.SimpleImputer(30)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(30)_strategy"constant"
sklearn.impute._base.SimpleImputer(30)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(31)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(31)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(31)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(31)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(31)_sparsefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_alpha0.0001
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_averagefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_class_weightnull
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_early_stoppingfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_epsilon0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_eta00.0
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_l1_ratio0.15
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_learning_rate"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_loss"hinge"
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_max_iter1000
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_n_iter_no_change5
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_n_jobsnull
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_penalty"l2"
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_power_t0.5
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_random_state0
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_tol0.001
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_validation_fraction0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(3)_warm_startfalse
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "encoder", "step_name": "encoder"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical", "step_name": "categorical"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "model", "step_name": "model"}}]
sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=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
0.5305 ± 0.0437
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5725 ± 0.0489
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0599 ± 0.076
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0013 ± 0.1746
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4309 ± 0.0751
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4507 ± 0.0026
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5691 ± 0.0751
Cross-validation details (10-fold Crossvalidation)
Number of instances
601
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5765 ± 0.0571
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5691 ± 0.0751
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9274 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9561 ± 0.1669
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4746 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.6565 ± 0.0576
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.3831 ± 0.1221
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5305 ± 0.0437
Cross-validation details (10-fold Crossvalidation)