Run
10589076

Run 10589076

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),kneighborsclassif ier=sklearn.neighbors._classification.KNeighborsClassifier)(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 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.neighbors._classification.KNeighborsClassifier(14)_algorithm"auto"
sklearn.neighbors._classification.KNeighborsClassifier(14)_leaf_size30
sklearn.neighbors._classification.KNeighborsClassifier(14)_metric"minkowski"
sklearn.neighbors._classification.KNeighborsClassifier(14)_metric_paramsnull
sklearn.neighbors._classification.KNeighborsClassifier(14)_n_jobsnull
sklearn.neighbors._classification.KNeighborsClassifier(14)_n_neighbors3
sklearn.neighbors._classification.KNeighborsClassifier(14)_p2
sklearn.neighbors._classification.KNeighborsClassifier(14)_weights"uniform"
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
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),kneighborsclassifier=sklearn.neighbors._classification.KNeighborsClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),kneighborsclassifier=sklearn.neighbors._classification.KNeighborsClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "kneighborsclassifier", "step_name": "kneighborsclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),kneighborsclassifier=sklearn.neighbors._classification.KNeighborsClassifier)(2)_verbosefalse

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
0.5433 ± 0.0463
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6034 ± 0.0459
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0286 ± 0.1092
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.031 ± 0.0704
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.404 ± 0.0234
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.62 ± 0.0499
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5924 ± 0.0485
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.62 ± 0.0499
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9615 ± 0.0557
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5168 ± 0.0214
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1278 ± 0.0467
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5133 ± 0.0506
Cross-validation details (10-fold Crossvalidation)