Run
10588990

Run 10588990

Task 31 (Supervised Classification) credit-g Uploaded 27-09-2022 by Laurens Krudde
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,one hotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclass ifier=sklearn.tree._classes.DecisionTreeClassifier)(5)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 it to `'passthrough'` or `None`.
sklearn.impute._base.SimpleImputer(34)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(34)_copytrue
sklearn.impute._base.SimpleImputer(34)_fill_valuenull
sklearn.impute._base.SimpleImputer(34)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(34)_strategy"mean"
sklearn.impute._base.SimpleImputer(34)_verbose"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(28)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(28)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(28)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(28)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(28)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(28)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(28)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(28)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(28)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(28)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(28)_random_state6837
sklearn.tree._classes.DecisionTreeClassifier(28)_splitter"best"
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(5)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(5)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(5)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}]
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_verbose_feature_names_outtrue
sklearn.preprocessing._encoders.OneHotEncoder(33)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(33)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(33)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(33)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(33)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(33)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(33)_sparsetrue

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

Area under ROC curve
0.6207 ± 0.0617
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6836 ± 0.0437
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2435 ± 0.1133
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1956 ± 0.1037
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.315 ± 0.0406
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.685 ± 0.0406
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6823 ± 0.0484
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.685 ± 0.0406
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7497 ± 0.0967
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5612 ± 0.038
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2247 ± 0.083
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6207 ± 0.0617
Cross-validation details (10-fold Crossvalidation)