OpenML
10593822

Run 10593822

Task 9899 (Supervised Classification) bank-marketing Uploaded 28-06-2023 by Luís Miguel Matos
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Flow

sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer .ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncode r),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(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.ensemble._forest.RandomForestClassifier(28)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(28)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(28)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(28)_criterion"log_loss"
sklearn.ensemble._forest.RandomForestClassifier(28)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(28)_max_features0.36774355827179483
sklearn.ensemble._forest.RandomForestClassifier(28)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(28)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(28)_min_impurity_decrease0.0002758626698001953
sklearn.ensemble._forest.RandomForestClassifier(28)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(28)_min_samples_split0.021169399427902462
sklearn.ensemble._forest.RandomForestClassifier(28)_min_weight_fraction_leaf0.007288822171589673
sklearn.ensemble._forest.RandomForestClassifier(28)_n_estimators137
sklearn.ensemble._forest.RandomForestClassifier(28)_n_jobs-1
sklearn.ensemble._forest.RandomForestClassifier(28)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(28)_random_state23992
sklearn.ensemble._forest.RandomForestClassifier(28)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(28)_warm_startfalse
sklearn.preprocessing._encoders.OneHotEncoder(43)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(43)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(43)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(43)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(43)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(43)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(43)_sparsefalse
sklearn.preprocessing._encoders.OneHotEncoder(43)_sparse_outputtrue
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_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),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder)(1)_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"}}}]
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose_feature_names_outtrue

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
0.7341 ± 0.0529
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8631 ± 0.0112
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2184 ± 0.0726
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0306 ± 0.0674
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1797 ± 0.0059
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2041 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8932 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
Number of instances
4521
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8714 ± 0.0137
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8932 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5155 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8806 ± 0.0298
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3193 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2981 ± 0.0091
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9337 ± 0.0296
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5732 ± 0.0268
Cross-validation details (10-fold Crossvalidation)