Run
10437757

Run 10437757

Task 48 (Supervised Classification) heart-c Uploaded 03-03-2020 by Fares Gaaloul
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sk learn.tree.tree.DecisionTreeClassifier)(8)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.impute.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_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.impute.SimpleImputer(17)_copytrue
sklearn.impute.SimpleImputer(17)_fill_valuenull
sklearn.impute.SimpleImputer(17)_missing_valuesNaN
sklearn.impute.SimpleImputer(17)_strategy"mean"
sklearn.impute.SimpleImputer(17)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(62)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(62)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(62)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(62)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(62)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(62)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(62)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(62)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(62)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(62)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(62)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(62)_random_state47560
sklearn.tree.tree.DecisionTreeClassifier(62)_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.

17 Evaluation measures

0.7109 ± 0.0666
Per class
Cross-validation details (10-fold Crossvalidation)
0.713 ± 0.067
Per class
Cross-validation details (10-fold Crossvalidation)
0.4215 ± 0.1354
Cross-validation details (10-fold Crossvalidation)
0.4273 ± 0.1328
Cross-validation details (10-fold Crossvalidation)
0.1149 ± 0.0266
Cross-validation details (10-fold Crossvalidation)
0.2004 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
0.7129 ± 0.0666
Cross-validation details (10-fold Crossvalidation)
303
Per class
Cross-validation details (10-fold Crossvalidation)
0.7131 ± 0.0691
Per class
Cross-validation details (10-fold Crossvalidation)
0.7129 ± 0.0666
Cross-validation details (10-fold Crossvalidation)
1.0084 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
0.5732 ± 0.1329
Cross-validation details (10-fold Crossvalidation)
0.315 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
0.3389 ± 0.043
Cross-validation details (10-fold Crossvalidation)
1.0759 ± 0.1361
Cross-validation details (10-fold Crossvalidation)