Run
10455931

Run 10455931

Task 9914 (Supervised Classification) wilt 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.ensemble._forest.RandomForestClassifier)(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.ensemble._forest.RandomForestClassifier(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.21195416384256027
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features1
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes1320
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.562041369543195
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.175124530960982
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.3147325138332431
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.3935433567539377
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.1452880363588121
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1847
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,step_1=sklearn.ensemble._forest.RandomForestClassifier)(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.ensemble._forest.RandomForestClassifier)(1)_verbosefalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_alpha1.0
sklearn.decomposition._kernel_pca.KernelPCA(1)_coef00.12011446609699994
sklearn.decomposition._kernel_pca.KernelPCA(1)_copy_Xfalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_degree6
sklearn.decomposition._kernel_pca.KernelPCA(1)_eigen_solver"arpack"
sklearn.decomposition._kernel_pca.KernelPCA(1)_fit_inverse_transformfalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_gamma1.7975872352042979e-09
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel"poly"
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel_paramsnull
sklearn.decomposition._kernel_pca.KernelPCA(1)_max_iter726
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_components3
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)_tol0.2717288731832881

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.

16 Evaluation measures

Area under ROC curve
0.4981
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0008 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1021 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9461 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Number of instances
4839
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9461 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3029 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9984 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)