Run
81100

Run 81100

Task 25 (Supervised Classification) mfeat-factors Uploaded 06-07-2020 by Test Test
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  • openml-python Sklearn_0.21.2. study_10966
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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_state12380
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.

18 Evaluation measures

0.873 ± 0.0223
Per class
Cross-validation details (10-fold Crossvalidation)
0.5923
Per class
0.5461 ± 0.0523
Cross-validation details (10-fold Crossvalidation)
0.5986 ± 0.0398
Cross-validation details (10-fold Crossvalidation)
0.0873 ± 0.007
Cross-validation details (10-fold Crossvalidation)
0.18
Cross-validation details (10-fold Crossvalidation)
0.5915 ± 0.047
Cross-validation details (10-fold Crossvalidation)
2000
Per class
Cross-validation details (10-fold Crossvalidation)
0.7106
Per class
0.5915 ± 0.047
Cross-validation details (10-fold Crossvalidation)
3.3219
Cross-validation details (10-fold Crossvalidation)
0.4853 ± 0.0389
Cross-validation details (10-fold Crossvalidation)
0.3
Cross-validation details (10-fold Crossvalidation)
0.2142 ± 0.0097
Cross-validation details (10-fold Crossvalidation)
0.7142 ± 0.0322
Cross-validation details (10-fold Crossvalidation)
0.5915 ± 0.047
Cross-validation details (10-fold Crossvalidation)