Run
10560205

Run 10560205

Task 9952 (Supervised Classification) phoneme Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standar dScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifie r(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTree Classifier=sklearn.tree.tree.ExtraTreeClassifier))(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.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_state3426
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_state3435
sklearn.tree.tree.ExtraTreeClassifier(28)_splitter"random"
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)(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"}}]
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)(2)_n_jobs1
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)(2)_voting"hard"
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)(2)_weightsnull
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier))(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "votingclassifier", "step_name": "votingclassifier"}}]

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

0.802 ± 0.0219
Per class
Cross-validation details (10-fold Crossvalidation)
0.8618 ± 0.0153
Per class
Cross-validation details (10-fold Crossvalidation)
0.6559 ± 0.039
Cross-validation details (10-fold Crossvalidation)
0.6585 ± 0.0349
Cross-validation details (10-fold Crossvalidation)
0.1314 ± 0.0135
Cross-validation details (10-fold Crossvalidation)
0.4147 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.8686 ± 0.0135
Cross-validation details (10-fold Crossvalidation)
5404
Per class
Cross-validation details (10-fold Crossvalidation)
0.8697 ± 0.0136
Per class
Cross-validation details (10-fold Crossvalidation)
0.8686 ± 0.0135
Cross-validation details (10-fold Crossvalidation)
0.8732 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.3168 ± 0.0324
Cross-validation details (10-fold Crossvalidation)
0.4554 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.3625 ± 0.0187
Cross-validation details (10-fold Crossvalidation)
0.796 ± 0.0408
Cross-validation details (10-fold Crossvalidation)
0.802 ± 0.0219
Cross-validation details (10-fold Crossvalidation)