Run
10714

Run 10714

Task 109 (Supervised Classification) segment Uploaded 07-11-2019 by Test Test
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  • openml-python Sklearn_0.21.2. study_1397
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.tree.tree.DecisionTreeClassifier)(7)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(16)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(16)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(16)_max_depth5
sklearn.tree.tree.DecisionTreeClassifier(16)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(16)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(16)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(16)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(16)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(16)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(16)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(16)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(16)_random_state37010
sklearn.tree.tree.DecisionTreeClassifier(16)_splitter"best"
sklearn.impute._base.SimpleImputer(7)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(7)_copytrue
sklearn.impute._base.SimpleImputer(7)_fill_valuenull
sklearn.impute._base.SimpleImputer(7)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(7)_strategy"mean"
sklearn.impute._base.SimpleImputer(7)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(7)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(7)_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)(7)_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.

16 Evaluation measures

0.9585 ± 0.0045
Per class
Cross-validation details (10-fold Crossvalidation)
0.796 ± 0.0169
Cross-validation details (10-fold Crossvalidation)
0.8247 ± 0.0121
Cross-validation details (10-fold Crossvalidation)
0.0615 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.2449
Cross-validation details (10-fold Crossvalidation)
0.8251 ± 0.0145
Cross-validation details (10-fold Crossvalidation)
2310
Per class
Cross-validation details (10-fold Crossvalidation)
0.8251 ± 0.0145
Cross-validation details (10-fold Crossvalidation)
2.8074
Cross-validation details (10-fold Crossvalidation)
0.2513 ± 0.0111
Cross-validation details (10-fold Crossvalidation)
0.3499
Cross-validation details (10-fold Crossvalidation)
0.1766 ± 0.007
Cross-validation details (10-fold Crossvalidation)
0.5047 ± 0.0201
Cross-validation details (10-fold Crossvalidation)
0.8251 ± 0.0145
Cross-validation details (10-fold Crossvalidation)