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10575867

Run 10575867

Task 15 (Supervised Classification) breast-w Uploaded 01-12-2021 by Marc Boel
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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),gradientboostingc lassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(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(25)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(25)_copytrue
sklearn.impute._base.SimpleImputer(25)_fill_valuenull
sklearn.impute._base.SimpleImputer(25)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(25)_strategy"median"
sklearn.impute._base.SimpleImputer(25)_verbose0
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_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)(1)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(29)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(29)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(29)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(29)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(29)_sparsetrue
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(1)_verbosefalse
sklearn.ensemble._gb.GradientBoostingClassifier(3)_ccp_alpha0.0
sklearn.ensemble._gb.GradientBoostingClassifier(3)_criterion"friedman_mse"
sklearn.ensemble._gb.GradientBoostingClassifier(3)_initnull
sklearn.ensemble._gb.GradientBoostingClassifier(3)_learning_rate0.25482340021476096
sklearn.ensemble._gb.GradientBoostingClassifier(3)_loss"deviance"
sklearn.ensemble._gb.GradientBoostingClassifier(3)_max_depth3
sklearn.ensemble._gb.GradientBoostingClassifier(3)_max_featuresnull
sklearn.ensemble._gb.GradientBoostingClassifier(3)_max_leaf_nodes1948
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_impurity_decrease0.0
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_impurity_splitnull
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_samples_leaf123
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_samples_split2
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_weight_fraction_leaf0.0
sklearn.ensemble._gb.GradientBoostingClassifier(3)_n_estimators100
sklearn.ensemble._gb.GradientBoostingClassifier(3)_n_iter_no_change17
sklearn.ensemble._gb.GradientBoostingClassifier(3)_random_state54452
sklearn.ensemble._gb.GradientBoostingClassifier(3)_subsample1.0
sklearn.ensemble._gb.GradientBoostingClassifier(3)_tol0.0001
sklearn.ensemble._gb.GradientBoostingClassifier(3)_validation_fraction0.36074252489868347
sklearn.ensemble._gb.GradientBoostingClassifier(3)_verbose0
sklearn.ensemble._gb.GradientBoostingClassifier(3)_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
0.9847 ± 0.0112
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9686 ± 0.0231
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9306 ± 0.0514
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8828 ± 0.0392
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0577 ± 0.0166
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4519 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9685 ± 0.0231
Cross-validation details (10-fold Crossvalidation)
Number of instances
699
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9687 ± 0.0232
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9685 ± 0.0231
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9293 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1277 ± 0.0368
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4753 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1712 ± 0.0457
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3602 ± 0.096
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9671 ± 0.0264
Cross-validation details (10-fold Crossvalidation)