Run
10560683

Run 10560683

Task 43 (Supervised Classification) spambase 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_state55632
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

0.92 ± 0.0151
Per class
Cross-validation details (10-fold Crossvalidation)
0.9223 ± 0.0146
Per class
Cross-validation details (10-fold Crossvalidation)
0.8376 ± 0.0305
Cross-validation details (10-fold Crossvalidation)
0.8331 ± 0.0309
Cross-validation details (10-fold Crossvalidation)
0.0781 ± 0.0145
Cross-validation details (10-fold Crossvalidation)
0.4776 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.9222 ± 0.0146
Cross-validation details (10-fold Crossvalidation)
4601
Per class
Cross-validation details (10-fold Crossvalidation)
0.9225 ± 0.0145
Per class
Cross-validation details (10-fold Crossvalidation)
0.9222 ± 0.0146
Cross-validation details (10-fold Crossvalidation)
0.9674 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.1636 ± 0.0303
Cross-validation details (10-fold Crossvalidation)
0.4886 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.2792 ± 0.0282
Cross-validation details (10-fold Crossvalidation)
0.5714 ± 0.0577
Cross-validation details (10-fold Crossvalidation)
0.9201 ± 0.0152
Cross-validation details (10-fold Crossvalidation)