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10589424

Run 10589424

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 28-09-2022 by VAIBHAV JAISWAL
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Flow

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklea rn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardSc aler),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.preprocessing._data.StandardScaler(11)_copytrue
sklearn.preprocessing._data.StandardScaler(11)_with_meantrue
sklearn.preprocessing._data.StandardScaler(11)_with_stdtrue
sklearn.impute._base.SimpleImputer(30)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(30)_copytrue
sklearn.impute._base.SimpleImputer(30)_fill_valuenull
sklearn.impute._base.SimpleImputer(30)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(30)_strategy"mean"
sklearn.impute._base.SimpleImputer(30)_verbose0
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_verbosefalse
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "numerical", "step_name": "numerical"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "model", "step_name": "model"}}]
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbosefalse
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_state42
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

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
0.571 ± 0.0136
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5681 ± 0.0356
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1484 ± 0.0266
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1811 ± 0.0218
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4033 ± 0.0107
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5967 ± 0.0107
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5961 ± 0.0415
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5967 ± 0.0107
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8151 ± 0.0217
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.635 ± 0.0085
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2768 ± 0.0171
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.571 ± 0.0136
Cross-validation details (10-fold Crossvalidation)