Run
10560240

Run 10560240

Task 167141 (Supervised Classification) churn Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imput er,classifier=sklearn.tree.tree.DecisionTreeClassifier)(3)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 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(52)_axis0
sklearn.preprocessing.imputation.Imputer(52)_copytrue
sklearn.preprocessing.imputation.Imputer(52)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(52)_strategy"median"
sklearn.preprocessing.imputation.Imputer(52)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(66)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(66)_criterion"entropy"
sklearn.tree.tree.DecisionTreeClassifier(66)_max_depth0.2801630246326896
sklearn.tree.tree.DecisionTreeClassifier(66)_max_features1.0
sklearn.tree.tree.DecisionTreeClassifier(66)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(66)_min_impurity_split1e-07
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_leaf18
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_split16
sklearn.tree.tree.DecisionTreeClassifier(66)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(66)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(66)_random_state16214
sklearn.tree.tree.DecisionTreeClassifier(66)_splitter"best"
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]

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

0.4983
Per class
Cross-validation details (10-fold Crossvalidation)
0.0003 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.2428 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.2429 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.8586 ± 0.001
Cross-validation details (10-fold Crossvalidation)
5000
Per class
Cross-validation details (10-fold Crossvalidation)
0.8586 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.5879 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
0.9996 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.3484 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.3484 ± 0.001
Cross-validation details (10-fold Crossvalidation)
1 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)