Run
10560115

Run 10560115

Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(featureunion=sklearn.pipeline.FeatureUnion(voting classifier=sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn. tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier) ,functiontransformer=sklearn.preprocessing._function_transformer.FunctionTr ansformer),logisticregression=sklearn.linear_model.logistic.LogisticRegress ion)(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.linear_model.logistic.LogisticRegression(36)_C1.0
sklearn.linear_model.logistic.LogisticRegression(36)_class_weightnull
sklearn.linear_model.logistic.LogisticRegression(36)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(36)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(36)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(36)_max_iter100
sklearn.linear_model.logistic.LogisticRegression(36)_multi_class"ovr"
sklearn.linear_model.logistic.LogisticRegression(36)_n_jobs1
sklearn.linear_model.logistic.LogisticRegression(36)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(36)_random_state63965
sklearn.linear_model.logistic.LogisticRegression(36)_solver"liblinear"
sklearn.linear_model.logistic.LogisticRegression(36)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(36)_verbose0
sklearn.linear_model.logistic.LogisticRegression(36)_warm_startfalse
sklearn.pipeline.Pipeline(featureunion=sklearn.pipeline.FeatureUnion(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier),functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer),logisticregression=sklearn.linear_model.logistic.LogisticRegression)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "featureunion", "step_name": "featureunion"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "logisticregression", "step_name": "logisticregression"}}]
sklearn.pipeline.FeatureUnion(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier),functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer)(2)_n_jobs1
sklearn.pipeline.FeatureUnion(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier),functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer)(2)_transformer_list[{"oml-python:serialized_object": "component_reference", "value": {"key": "votingclassifier", "step_name": "votingclassifier"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "functiontransformer", "step_name": "functiontransformer"}}]
sklearn.pipeline.FeatureUnion(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier),functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer)(2)_transformer_weightsnull
sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier)(2)_estimators[{"oml-python:serialized_object": "component_reference", "value": {"key": "dtc", "step_name": "dtc"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "etc", "step_name": "etc"}}]
sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier)(2)_n_jobs1
sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier)(2)_voting"hard"
sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier)(2)_weightsnull
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_state29159
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_state57883
sklearn.tree.tree.ExtraTreeClassifier(28)_splitter"random"
sklearn.preprocessing._function_transformer.FunctionTransformer(5)_accept_sparsefalse
sklearn.preprocessing._function_transformer.FunctionTransformer(5)_funcnull
sklearn.preprocessing._function_transformer.FunctionTransformer(5)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.FunctionTransformer(5)_inverse_funcnull
sklearn.preprocessing._function_transformer.FunctionTransformer(5)_kw_argsnull
sklearn.preprocessing._function_transformer.FunctionTransformer(5)_pass_yfalse
sklearn.preprocessing._function_transformer.FunctionTransformer(5)_validatetrue

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
0.6521 ± 0.0807
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7175 ± 0.0579
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1985 ± 0.1659
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0687 ± 0.1489
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3103 ± 0.0438
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.363 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7273 ± 0.0553
Cross-validation details (10-fold Crossvalidation)
Number of instances
748
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7105 ± 0.0616
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7273 ± 0.0553
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7916 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8549 ± 0.1206
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4258 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4745 ± 0.0495
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1143 ± 0.1163
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5931 ± 0.0809
Cross-validation details (10-fold Crossvalidation)