Run
10582732

Run 10582732

Task 3022 (Supervised Classification) vowel 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"most_frequent"
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_rate0.12639069401337502
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_nodes345
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_impurity_decrease0.0
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_impurity_splitnull
sklearn.ensemble._gb.GradientBoostingClassifier(3)_min_samples_leaf74
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_change7
sklearn.ensemble._gb.GradientBoostingClassifier(3)_random_state30383
sklearn.ensemble._gb.GradientBoostingClassifier(3)_subsample1.0
sklearn.ensemble._gb.GradientBoostingClassifier(3)_tol0.0001
sklearn.ensemble._gb.GradientBoostingClassifier(3)_validation_fraction0.3903692616392492
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
0.4783 ± 0.0171
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.0242 ± 0.0149
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0733 ± 0.0167
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0012 ± 0.0157
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1757 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1653
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.0242 ± 0.0152
Cross-validation details (10-fold Crossvalidation)
Number of instances
990
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.0243 ± 0.0148
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.0242 ± 0.0152
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.4594
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0632 ± 0.0134
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2875
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3914 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.3614 ± 0.0159
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.0242 ± 0.0152
Cross-validation details (10-fold Crossvalidation)