Run
10560414

Run 10560414

Task 146817 (Supervised Classification) steel-plates-fault Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,randomforestclassifier =sklearn.ensemble.forest.RandomForestClassifier)(2)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 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 to None.
sklearn.ensemble.forest.RandomForestClassifier(67)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(67)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(67)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(67)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(67)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(67)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(67)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(67)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(67)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(67)_random_state13398
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_startfalse
sklearn.preprocessing.imputation.Imputer(52)_axis0
sklearn.preprocessing.imputation.Imputer(52)_copytrue
sklearn.preprocessing.imputation.Imputer(52)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(52)_strategy"median"
sklearn.preprocessing.imputation.Imputer(52)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "kernelpca", "step_name": "kernelpca"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.decomposition.kernel_pca.KernelPCA(2)_alpha1.0
sklearn.decomposition.kernel_pca.KernelPCA(2)_coef01
sklearn.decomposition.kernel_pca.KernelPCA(2)_copy_Xtrue
sklearn.decomposition.kernel_pca.KernelPCA(2)_degree3
sklearn.decomposition.kernel_pca.KernelPCA(2)_eigen_solver"auto"
sklearn.decomposition.kernel_pca.KernelPCA(2)_fit_inverse_transformfalse
sklearn.decomposition.kernel_pca.KernelPCA(2)_gammanull
sklearn.decomposition.kernel_pca.KernelPCA(2)_kernel"rbf"
sklearn.decomposition.kernel_pca.KernelPCA(2)_kernel_paramsnull
sklearn.decomposition.kernel_pca.KernelPCA(2)_max_iternull
sklearn.decomposition.kernel_pca.KernelPCA(2)_n_componentsnull
sklearn.decomposition.kernel_pca.KernelPCA(2)_n_jobs-1
sklearn.decomposition.kernel_pca.KernelPCA(2)_random_state20947
sklearn.decomposition.kernel_pca.KernelPCA(2)_remove_zero_eigfalse
sklearn.decomposition.kernel_pca.KernelPCA(2)_tol0

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
0.4988 ± 0.0015
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1521 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.187 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.3467 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Number of instances
1941
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.3467 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.4107 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.841 ± 0.0033
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4311 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2931 ± 0.0104
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.1429
Cross-validation details (10-fold Crossvalidation)