Run
10454461

Run 10454461

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._kernel_pca.KernelPC A,step_1=sklearn.svm._classes.SVC)(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.svm._classes.SVC(4)_C0.012928104270297176
sklearn.svm._classes.SVC(4)_break_tiestrue
sklearn.svm._classes.SVC(4)_cache_size200
sklearn.svm._classes.SVC(4)_class_weightnull
sklearn.svm._classes.SVC(4)_coef020.115942731692257
sklearn.svm._classes.SVC(4)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(4)_degree2
sklearn.svm._classes.SVC(4)_gamma1.0316939984838152e-07
sklearn.svm._classes.SVC(4)_kernel"poly"
sklearn.svm._classes.SVC(4)_max_iter-1
sklearn.svm._classes.SVC(4)_probabilitytrue
sklearn.svm._classes.SVC(4)_random_state42
sklearn.svm._classes.SVC(4)_shrinkingtrue
sklearn.svm._classes.SVC(4)_tol0.0001984356207233966
sklearn.svm._classes.SVC(4)_verbosefalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_alpha4
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"arpack"
sklearn.decomposition._kernel_pca.KernelPCA(1)_fit_inverse_transformtrue
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_iter524
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_components4
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_jobs1
sklearn.decomposition._kernel_pca.KernelPCA(1)_random_state42
sklearn.decomposition._kernel_pca.KernelPCA(1)_remove_zero_eigfalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_tol0.6476998368285369
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,step_1=sklearn.svm._classes.SVC)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,step_1=sklearn.svm._classes.SVC)(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.svm._classes.SVC)(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

0.5034 ± 0.0305
Per class
Cross-validation details (10-fold Crossvalidation)
0.496
Per class
0.0021
0.0001 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
0.501 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
3848
Per class
Cross-validation details (10-fold Crossvalidation)
0.5011
Per class
0.501 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
1
Cross-validation details (10-fold Crossvalidation)
0.9999 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
0.5002 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
1.0004 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.501
Cross-validation details (10-fold Crossvalidation)