Run
10454552

Run 10454552

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._truncated_svd.Trunc atedSVD,step_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)_alpha7.741248157635298e-05
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)_epsilon44.805395655443036
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.9222877048801364
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.6667106411122504
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"log"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter735
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change10
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"elasticnet"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.8340533988005889
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.9421633514061051
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.7903374659482537
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_algorithm"randomized"
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_components1
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_iter69
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_random_state42
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_tol0.0
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,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._truncated_svd.TruncatedSVD,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
0.4934 ± 0.0267
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4922 ± 0.0299
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0125 ± 0.0562
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0005 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.5002 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.5
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.4938 ± 0.0281
Cross-validation details (10-fold Crossvalidation)
Number of instances
3848
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4937 ± 0.0298
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4938 ± 0.0281
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0004 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5005 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0011 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4938 ± 0.0281
Cross-validation details (10-fold Crossvalidation)