Run
10453381

Run 10453381

Task 9946 (Supervised Classification) wdbc Uploaded 18-05-2020 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.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.pipeline.Pipeline(step_0=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbosefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_alpha1.5506038158237694
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)_epsilon43.4321776084078
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.03935680279935779
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.7248766195839688
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"epsilon_insensitive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter571
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change84
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_t0.41299791274045683
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.583867452056236
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.3224727739115999
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_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.

16 Evaluation measures

Area under ROC curve
0.5
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1802 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3726 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4676 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6274 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Number of instances
569
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6274 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9526 ± 0.0055
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7967 ± 0.0125
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4835 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.6104 ± 0.006
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2625 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)