Run
10459694

Run 10459694

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 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)_alpha0.0035546647087795775
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)_epsilon0.7931453860864989
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.3129354955086644
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.7628286563442891
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"modified_huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1636
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change4
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_t0.47319084983793985
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.424448811157895
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.3753692300617467
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_iter473
sklearn.decomposition._fastica.FastICA(1)_n_components17
sklearn.decomposition._fastica.FastICA(1)_random_state42
sklearn.decomposition._fastica.FastICA(1)_tol0.6524369366268011
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
0.4939 ± 0.0378
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5756 ± 0.055
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0126 ± 0.0738
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0333 ± 0.2342
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4138 ± 0.0938
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4271 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5861 ± 0.094
Cross-validation details (10-fold Crossvalidation)
Number of instances
16599
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5675 ± 0.0374
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5861 ± 0.094
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8921 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.969 ± 0.2195
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4621 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.6428 ± 0.0725
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.391 ± 0.1568
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.494 ± 0.0374
Cross-validation details (10-fold Crossvalidation)