Run
9750

Run 9750

Task 115 (Supervised Classification) diabetes Uploaded 07-11-2019 by Test Test
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  • openml-python Sklearn_0.21.2. study_1277
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.tree.tree.DecisionTreeClassifier)(5)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 transformers in the pipeline can be cached using ``memory`` argument. 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 it to 'passthrough' or ``None``.
sklearn.tree.tree.DecisionTreeClassifier(11)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(11)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(11)_max_depth5
sklearn.tree.tree.DecisionTreeClassifier(11)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(11)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(11)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(11)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(11)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(11)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(11)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(11)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(11)_random_state60732
sklearn.tree.tree.DecisionTreeClassifier(11)_splitter"best"
sklearn.impute._base.SimpleImputer(5)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(5)_copytrue
sklearn.impute._base.SimpleImputer(5)_fill_valuenull
sklearn.impute._base.SimpleImputer(5)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(5)_strategy"mean"
sklearn.impute._base.SimpleImputer(5)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(5)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(5)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(5)_verbosefalse

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

0.8014 ± 0.0481
Per class
Cross-validation details (10-fold Crossvalidation)
0.7448 ± 0.0297
Per class
Cross-validation details (10-fold Crossvalidation)
0.4317 ± 0.071
Cross-validation details (10-fold Crossvalidation)
0.3586 ± 0.0627
Cross-validation details (10-fold Crossvalidation)
0.2907 ± 0.0271
Cross-validation details (10-fold Crossvalidation)
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.7487 ± 0.0253
Cross-validation details (10-fold Crossvalidation)
768
Per class
Cross-validation details (10-fold Crossvalidation)
0.7434 ± 0.0259
Per class
Cross-validation details (10-fold Crossvalidation)
0.7487 ± 0.0253
Cross-validation details (10-fold Crossvalidation)
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.6396 ± 0.0601
Cross-validation details (10-fold Crossvalidation)
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.4208 ± 0.0245
Cross-validation details (10-fold Crossvalidation)
0.8828 ± 0.0526
Cross-validation details (10-fold Crossvalidation)
0.71 ± 0.0411
Cross-validation details (10-fold Crossvalidation)