Run
12380

Run 12380

Task 463 (Supervised Classification) one-hundred-plants-margin Uploaded 08-11-2019 by Test Test
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  • openml-python Sklearn_0.21.2. study_1610
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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_state35915
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

0.731 ± 0.0452
Per class
Cross-validation details (10-fold Crossvalidation)
0.0593 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
0.1911 ± 0.0347
Cross-validation details (10-fold Crossvalidation)
0.0186 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.0198
Cross-validation details (10-fold Crossvalidation)
0.0688 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
1600
Per class
Cross-validation details (10-fold Crossvalidation)
0.0688 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
6.6439
Cross-validation details (10-fold Crossvalidation)
0.9394 ± 0.0089
Cross-validation details (10-fold Crossvalidation)
0.0995
Cross-validation details (10-fold Crossvalidation)
0.0969 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.9737 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
0.0688 ± 0.0104
Cross-validation details (10-fold Crossvalidation)