Run
10560328

Run 10560328

Task 10093 (Supervised Classification) banknote-authentication Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(votingclassifier=sklearn.ensemble.voting_classifi er.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standa rdscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=s klearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.t ree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClas sifier,RFC=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_state13733
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_startfalse
sklearn.preprocessing.data.StandardScaler(43)_copytrue
sklearn.preprocessing.data.StandardScaler(43)_with_meantrue
sklearn.preprocessing.data.StandardScaler(43)_with_stdtrue
sklearn.tree.tree.DecisionTreeClassifier(66)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(66)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(66)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(66)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(66)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(66)_min_impurity_split1e-07
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(66)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(66)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(66)_random_state64476
sklearn.tree.tree.DecisionTreeClassifier(66)_splitter"best"
sklearn.tree.tree.ExtraTreeClassifier(28)_class_weightnull
sklearn.tree.tree.ExtraTreeClassifier(28)_criterion"gini"
sklearn.tree.tree.ExtraTreeClassifier(28)_max_depth1000
sklearn.tree.tree.ExtraTreeClassifier(28)_max_features"auto"
sklearn.tree.tree.ExtraTreeClassifier(28)_max_leaf_nodesnull
sklearn.tree.tree.ExtraTreeClassifier(28)_min_impurity_split1e-07
sklearn.tree.tree.ExtraTreeClassifier(28)_min_samples_leaf1
sklearn.tree.tree.ExtraTreeClassifier(28)_min_samples_split2
sklearn.tree.tree.ExtraTreeClassifier(28)_min_weight_fraction_leaf0.0
sklearn.tree.tree.ExtraTreeClassifier(28)_random_state49480
sklearn.tree.tree.ExtraTreeClassifier(28)_splitter"random"
sklearn.pipeline.Pipeline(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier))(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "votingclassifier", "step_name": "votingclassifier"}}]
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)(2)_estimators[{"oml-python:serialized_object": "component_reference", "value": {"key": "DecisionTreeClassifier", "step_name": "DecisionTreeClassifier"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "ExtraTreeClassifier", "step_name": "ExtraTreeClassifier"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "KNN", "step_name": "KNN"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "RFC", "step_name": "RFC"}}]
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)(2)_n_jobs1
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)(2)_voting"soft"
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)(2)_weightsnull
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.neighbors.classification.KNeighborsClassifier(44)_algorithm"auto"
sklearn.neighbors.classification.KNeighborsClassifier(44)_leaf_size30
sklearn.neighbors.classification.KNeighborsClassifier(44)_metric"minkowski"
sklearn.neighbors.classification.KNeighborsClassifier(44)_metric_paramsnull
sklearn.neighbors.classification.KNeighborsClassifier(44)_n_jobs1
sklearn.neighbors.classification.KNeighborsClassifier(44)_n_neighbors1
sklearn.neighbors.classification.KNeighborsClassifier(44)_p2
sklearn.neighbors.classification.KNeighborsClassifier(44)_weights"uniform"

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
1
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9978 ± 0.0035
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9956 ± 0.0071
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9786 ± 0.0138
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0125 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4939 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9978 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
Number of instances
1372
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9978 ± 0.0035
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9978 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9911 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0253 ± 0.0155
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
0.0578 ± 0.0265
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.1162 ± 0.0534
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.998 ± 0.0032
Cross-validation details (10-fold Crossvalidation)