Run
10454267

Run 10454267

Task 3735 (Supervised Classification) pollen 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.decomposition._fastica.FastICA,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)_alpha1.291861804153882
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_averagetrue
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)_epsilon49.754145375929966
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.8925764490427657
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.7409426867564587
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"squared_loss"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter2388
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change51
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"l2"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.997828457752812
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.05221356056648424
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.8316098723128338
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.decomposition._fastica.FastICA(1)_algorithm"deflation"
sklearn.decomposition._fastica.FastICA(1)_fun"exp"
sklearn.decomposition._fastica.FastICA(1)_fun_argsnull
sklearn.decomposition._fastica.FastICA(1)_max_iter297
sklearn.decomposition._fastica.FastICA(1)_n_components4
sklearn.decomposition._fastica.FastICA(1)_random_state42
sklearn.decomposition._fastica.FastICA(1)_tol0.051729889556082674
sklearn.decomposition._fastica.FastICA(1)_w_initnull
sklearn.decomposition._fastica.FastICA(1)_whitenfalse
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,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.decomposition._fastica.FastICA,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
0.5047 ± 0.029
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4999 ± 0.0387
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0094 ± 0.0581
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0094 ± 0.058
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4953 ± 0.029
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.5
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5047 ± 0.029
Cross-validation details (10-fold Crossvalidation)
Number of instances
3848
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5049 ± 0.0318
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5047 ± 0.029
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9906 ± 0.058
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.7038 ± 0.0205
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.4076 ± 0.0411
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5047 ± 0.029
Cross-validation details (10-fold Crossvalidation)