Run
10560503

Run 10560503

Task 146817 (Supervised Classification) steel-plates-fault Uploaded 14-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, estimator=sklearn.tree.tree.DecisionTreeClassifier)(22)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 to None.
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(22)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(22)_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.preprocessing.imputation.Imputer(53)_axis0
sklearn.preprocessing.imputation.Imputer(53)_copytrue
sklearn.preprocessing.imputation.Imputer(53)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(53)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(53)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(67)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(67)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(67)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(67)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(67)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(67)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(67)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(67)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(67)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(67)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(67)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(67)_random_state4171
sklearn.tree.tree.DecisionTreeClassifier(67)_splitter"best"

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

0.8118 ± 0.0186
Per class
Cross-validation details (10-fold Crossvalidation)
0.7184 ± 0.028
Per class
Cross-validation details (10-fold Crossvalidation)
0.6367 ± 0.0352
Cross-validation details (10-fold Crossvalidation)
0.6809 ± 0.0316
Cross-validation details (10-fold Crossvalidation)
0.0807 ± 0.0079
Cross-validation details (10-fold Crossvalidation)
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.7177 ± 0.0276
Cross-validation details (10-fold Crossvalidation)
1941
Per class
Cross-validation details (10-fold Crossvalidation)
0.7193 ± 0.0273
Per class
Cross-validation details (10-fold Crossvalidation)
0.7177 ± 0.0276
Cross-validation details (10-fold Crossvalidation)
2.4107 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
0.3628 ± 0.0354
Cross-validation details (10-fold Crossvalidation)
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.284 ± 0.0139
Cross-validation details (10-fold Crossvalidation)
0.852 ± 0.0415
Cross-validation details (10-fold Crossvalidation)
0.7367 ± 0.0375
Cross-validation details (10-fold Crossvalidation)