Run
10595096

Run 10595096

Task 31 (Supervised Classification) credit-g Uploaded 22-04-2024 by Soheila Farokhi
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Flow

sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer .ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncode r,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensembl e._forest.RandomForestClassifier)(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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
sklearn.preprocessing._encoders.OneHotEncoder(50)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(50)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(50)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(50)_feature_name_combiner"concat"
sklearn.preprocessing._encoders.OneHotEncoder(50)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(50)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(50)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(50)_sparsefalse
sklearn.preprocessing._encoders.OneHotEncoder(50)_sparse_outputtrue
sklearn.impute._base.SimpleImputer(54)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(54)_copytrue
sklearn.impute._base.SimpleImputer(54)_fill_valuenull
sklearn.impute._base.SimpleImputer(54)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(54)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(54)_strategy"median"
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(5)_memorynull
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(5)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Preprocessing", "step_name": "Preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Classifier", "step_name": "Classifier"}}]
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(5)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(6)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(6)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(6)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(6)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(6)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical", "step_name": "categorical", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "continuous", "step_name": "continuous", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}]
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(6)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(6)_verbose_feature_names_outtrue
sklearn.ensemble._forest.RandomForestClassifier(43)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(43)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(43)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(43)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(43)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(43)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(43)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(43)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(43)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(43)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(43)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(43)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(43)_n_estimators10
sklearn.ensemble._forest.RandomForestClassifier(43)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(43)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(43)_random_state7823
sklearn.ensemble._forest.RandomForestClassifier(43)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(43)_warm_startfalse

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
0.7562 ± 0.043
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7228 ± 0.0435
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3122 ± 0.1096
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1907 ± 0.0424
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3347 ± 0.0136
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.748 ± 0.0346
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7314 ± 0.0502
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.748 ± 0.0346
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7966 ± 0.0323
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4166 ± 0.0177
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9092 ± 0.0387
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6362 ± 0.051
Cross-validation details (10-fold Crossvalidation)