Run
10461968

Run 10461968

Task 3891 (Supervised Classification) gina_agnostic Uploaded 20-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • automl_meta_features openml-python Sklearn_0.22.1.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.GenericUnivariateSelect,step_1=sklearn.discriminant_analysis.LinearDi scriminantAnalysis)(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.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components302
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkagenull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"svd"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.00017231190769363995
sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_mode"fdr"
sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_param0.41665016240906816
sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.f_classif"}
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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
0.9194 ± 0.017
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8497 ± 0.0228
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6993 ± 0.0455
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6578 ± 0.0369
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1764 ± 0.0178
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8498 ± 0.0228
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8499 ± 0.0226
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8498 ± 0.0228
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3529 ± 0.0356
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.341 ± 0.0265
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.682 ± 0.053
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8495 ± 0.0227
Cross-validation details (10-fold Crossvalidation)