Run
10560143

Run 10560143

Task 10093 (Supervised Classification) banknote-authentication Uploaded 13-08-2021 by Sergey Redyuk
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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_state20623
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_state53020
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.

18 Evaluation measures

Area under ROC curve
0.9951 ± 0.0044
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9709 ± 0.0108
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9411 ± 0.0218
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6673 ± 0.0539
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1883 ± 0.028
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4939 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9708 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
Number of instances
1372
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9712 ± 0.0104
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9708 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9911 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3814 ± 0.0568
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4969 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2433 ± 0.0271
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4897 ± 0.0545
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9718 ± 0.0099
Cross-validation details (10-fold Crossvalidation)