Run
10464842

Run 10464842

Task 31 (Supervised Classification) credit-g Uploaded 18-06-2020 by Richard Cook
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.tree.tree.DecisionTreeClassifier)(6)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(58)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(58)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(58)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(58)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(58)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(58)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(58)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(58)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(58)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(58)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(58)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(58)_random_state21867
sklearn.tree.tree.DecisionTreeClassifier(58)_splitter"best"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(6)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(6)_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)(6)_verbosefalse
sklearn.impute._base.SimpleImputer(10)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(10)_copytrue
sklearn.impute._base.SimpleImputer(10)_fill_valuenull
sklearn.impute._base.SimpleImputer(10)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(10)_strategy"mean"
sklearn.impute._base.SimpleImputer(10)_verbose0

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

0.6429 ± 0.0437
Per class
Cross-validation details (10-fold Crossvalidation)
0.692 ± 0.0335
Per class
Cross-validation details (10-fold Crossvalidation)
0.2778 ± 0.0826
Cross-validation details (10-fold Crossvalidation)
0.2033 ± 0.0858
Cross-validation details (10-fold Crossvalidation)
0.312 ± 0.0336
Cross-validation details (10-fold Crossvalidation)
0.4202
Cross-validation details (10-fold Crossvalidation)
0.688 ± 0.0336
Cross-validation details (10-fold Crossvalidation)
1000
Per class
Cross-validation details (10-fold Crossvalidation)
0.6972 ± 0.0358
Per class
Cross-validation details (10-fold Crossvalidation)
0.688 ± 0.0336
Cross-validation details (10-fold Crossvalidation)
0.8813
Cross-validation details (10-fold Crossvalidation)
0.7426 ± 0.08
Cross-validation details (10-fold Crossvalidation)
0.4583
Cross-validation details (10-fold Crossvalidation)
0.5586 ± 0.0296
Cross-validation details (10-fold Crossvalidation)
1.2189 ± 0.0647
Cross-validation details (10-fold Crossvalidation)
0.6429 ± 0.0437
Cross-validation details (10-fold Crossvalidation)