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10435755

Run 10435755

Task 189354 (Supervised Classification) airlines Uploaded 11-01-2020 by Alex Imbrea
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sklearn.pipeline.Pipeline(trasformer=sklearn.compose._column_transformer.Co lumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.i mpute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEnco der)),estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifi er)(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(10)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(10)_copytrue
sklearn.impute._base.SimpleImputer(10)_fill_valuenull
sklearn.impute._base.SimpleImputer(10)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(10)_strategy"constant"
sklearn.impute._base.SimpleImputer(10)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(17)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(17)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(17)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(17)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(17)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(17)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(17)_sparsetrue
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_learning_rate0.1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_max_depth3
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_max_featuresnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_min_impurity_decrease0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_min_impurity_splitnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_min_samples_leaf1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_min_samples_split2
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_min_weight_fraction_leaf0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_n_estimators100
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_n_iter_no_changenull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_random_state15915
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_subsample1.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_tol0.0001
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_validation_fraction0.1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(24)_warm_startfalse
sklearn.pipeline.Pipeline(trasformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder)),estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(trasformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder)),estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "trasformer", "step_name": "trasformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(trasformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder)),estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder))(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder))(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder))(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [true, false, true, true, true, false, false]}}]
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder))(1)_verbosefalse
sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical_inputer", "step_name": "categorical_inputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehot", "step_name": "onehot"}}]
sklearn.pipeline.Pipeline(categorical_inputer=sklearn.impute._base.SimpleImputer,onehot=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse

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
0.6664 ± 0.0026
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6099 ± 0.0019
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2286 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0935 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4566 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.494 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6363 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
Number of instances
539383
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6459 ± 0.0023
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6363 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9914 ± 0
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9242 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.497 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4752 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9562 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6091 ± 0.0018
Cross-validation details (10-fold Crossvalidation)