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10580609

Run 10580609

Task 58 (Supervised Classification) waveform-5000 Uploaded 02-12-2021 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"mean"
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_rate0.022627263208991978
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_nodes602
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_impurity_decrease0.0
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_impurity_splitnull
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_samples_leaf65
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_change14
sklearn.ensemble._gb.GradientBoostingClassifier(3)_random_state53077
sklearn.ensemble._gb.GradientBoostingClassifier(3)_subsample1.0
sklearn.ensemble._gb.GradientBoostingClassifier(3)_tol0.0001
sklearn.ensemble._gb.GradientBoostingClassifier(3)_validation_fraction0.13577012874646646
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
0.961 ± 0.0052
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8381 ± 0.0116
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.758 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6125 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2136 ± 0.005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4444 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8386 ± 0.0115
Cross-validation details (10-fold Crossvalidation)
Number of instances
5000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8392 ± 0.0112
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8386 ± 0.0115
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.5849 ± 0
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4806 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2866 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.608 ± 0.014
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.839 ± 0.0115
Cross-validation details (10-fold Crossvalidation)