Run
10454228

Run 10454228

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._pca.PCA,step_1=skle arn.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)_alpha6.766543865104033e-06
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)_epsilon48.24108866332984
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.40643186573579165
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"perceptron"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter398
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change42
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_t0.6230453448162371
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.17929753128160003
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.05716056884290199
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.decomposition._pca.PCA(1)_copyfalse
sklearn.decomposition._pca.PCA(1)_iterated_power"auto"
sklearn.decomposition._pca.PCA(1)_n_components1
sklearn.decomposition._pca.PCA(1)_random_state42
sklearn.decomposition._pca.PCA(1)_svd_solver"full"
sklearn.decomposition._pca.PCA(1)_tol0.5031374802518812
sklearn.decomposition._pca.PCA(1)_whitentrue
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,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._pca.PCA,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
0.5049 ± 0.0265
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5049 ± 0.0269
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0099 ± 0.053
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0099 ± 0.0531
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4951 ± 0.0265
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.5
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5049 ± 0.0265
Cross-validation details (10-fold Crossvalidation)
Number of instances
3848
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5049 ± 0.0266
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5049 ± 0.0265
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9901 ± 0.0531
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.7036 ± 0.0188
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.4072 ± 0.0376
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5049 ± 0.0265
Cross-validation details (10-fold Crossvalidation)