Run
10462147

Run 10462147

Task 3891 (Supervised Classification) gina_agnostic 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._kernel_pca.KernelPC A,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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_components89
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.9837394429982402
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"eigen"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.010837274077836287
sklearn.decomposition._kernel_pca.KernelPCA(1)_alpha1.0
sklearn.decomposition._kernel_pca.KernelPCA(1)_coef01
sklearn.decomposition._kernel_pca.KernelPCA(1)_copy_Xfalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_degree3
sklearn.decomposition._kernel_pca.KernelPCA(1)_eigen_solver"dense"
sklearn.decomposition._kernel_pca.KernelPCA(1)_fit_inverse_transformfalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_gammanull
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel"cosine"
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel_paramsnull
sklearn.decomposition._kernel_pca.KernelPCA(1)_max_iter913
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_components757
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_jobs1
sklearn.decomposition._kernel_pca.KernelPCA(1)_random_state42
sklearn.decomposition._kernel_pca.KernelPCA(1)_remove_zero_eigtrue
sklearn.decomposition._kernel_pca.KernelPCA(1)_tol0
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,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.decomposition._kernel_pca.KernelPCA,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
0.8959 ± 0.0167
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8161 ± 0.0113
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6322 ± 0.0227
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.634 ± 0.0229
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.183 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8163 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8171 ± 0.0117
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8163 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3661 ± 0.0232
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4139 ± 0.0145
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.828 ± 0.029
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8158 ± 0.0113
Cross-validation details (10-fold Crossvalidation)