Run
10591867

Run 10591867

Task 31 (Supervised Classification) credit-g Uploaded 21-02-2023 by Saurav Das
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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),reduce_dim=sklearn.feature _selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.R andomForestClassifier),clf=sklearn.ensemble._weight_boosting.AdaBoostClassi fier)(1)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(36)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(36)_copytrue
sklearn.impute._base.SimpleImputer(36)_fill_valuenull
sklearn.impute._base.SimpleImputer(36)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(36)_strategy"median"
sklearn.impute._base.SimpleImputer(36)_verbose"deprecated"
sklearn.ensemble._forest.RandomForestClassifier(23)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(23)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(23)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(23)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(23)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(23)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(23)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(23)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(23)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(23)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(23)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(23)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(23)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(23)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(23)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(23)_random_state9417
sklearn.ensemble._forest.RandomForestClassifier(23)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(23)_warm_startfalse
sklearn.feature_selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_importance_getter"auto"
sklearn.feature_selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_max_features9
sklearn.feature_selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_norm_order1
sklearn.feature_selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_prefitfalse
sklearn.feature_selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_threshold-Infinity
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(5)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(5)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(5)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(5)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(5)_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)(5)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(5)_verbose_feature_names_outtrue
sklearn.preprocessing._encoders.OneHotEncoder(38)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(38)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(38)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(38)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(38)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(38)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(38)_sparsefalse
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),reduce_dim=sklearn.feature_selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.RandomForestClassifier),clf=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),reduce_dim=sklearn.feature_selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.RandomForestClassifier),clf=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Preprocessing", "step_name": "Preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "reduce_dim", "step_name": "reduce_dim"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "clf", "step_name": "clf"}}]
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),reduce_dim=sklearn.feature_selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.RandomForestClassifier),clf=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbosefalse
sklearn.ensemble._weight_boosting.AdaBoostClassifier(5)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(5)_base_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(5)_learning_rate1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(5)_n_estimators80
sklearn.ensemble._weight_boosting.AdaBoostClassifier(5)_random_state0

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
0.7286 ± 0.0641
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7207 ± 0.0425
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3163 ± 0.0991
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.3196 ± 0.005
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4959 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.732 ± 0.0457
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7172 ± 0.045
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.732 ± 0.0457
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.1803 ± 0.0033
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4962 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0827 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6476 ± 0.0463
Cross-validation details (10-fold Crossvalidation)