Run
10560673

Run 10560673

Task 146817 (Supervised Classification) steel-plates-fault Uploaded 21-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imput er,estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)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.preprocessing.imputation.Imputer(54)_axis0
sklearn.preprocessing.imputation.Imputer(54)_copytrue
sklearn.preprocessing.imputation.Imputer(54)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(54)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(54)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(68)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(68)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(68)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(68)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(68)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(68)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(68)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(68)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(68)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(68)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(68)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(68)_random_state40525
sklearn.tree.tree.DecisionTreeClassifier(68)_splitter"best"
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]

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

0.8099 ± 0.0153
Per class
Cross-validation details (10-fold Crossvalidation)
0.7147 ± 0.0246
Per class
Cross-validation details (10-fold Crossvalidation)
0.6325 ± 0.0282
Cross-validation details (10-fold Crossvalidation)
0.6781 ± 0.025
Cross-validation details (10-fold Crossvalidation)
0.0817 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.7141 ± 0.0219
Cross-validation details (10-fold Crossvalidation)
1941
Per class
Cross-validation details (10-fold Crossvalidation)
0.7159 ± 0.0266
Per class
Cross-validation details (10-fold Crossvalidation)
0.7141 ± 0.0219
Cross-validation details (10-fold Crossvalidation)
2.4107 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
0.3674 ± 0.0281
Cross-validation details (10-fold Crossvalidation)
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.2858 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
0.8574 ± 0.0323
Cross-validation details (10-fold Crossvalidation)
0.7383 ± 0.0268
Cross-validation details (10-fold Crossvalidation)