Run
10216886

Run 10216886

Task 53 (Supervised Classification) vehicle Uploaded 18-04-2019 by Jan van Rijn
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.pr eprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.St andardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.imput e.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder )),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceT hreshold,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.Grad ientBoostingClassifier)(4)Automatically created scikit-learn flow.
sklearn.impute.SimpleImputer(10)_copytrue
sklearn.impute.SimpleImputer(10)_fill_value-1
sklearn.impute.SimpleImputer(10)_missing_valuesNaN
sklearn.impute.SimpleImputer(10)_strategy"constant"
sklearn.impute.SimpleImputer(10)_verbose0
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(4)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
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sklearn.preprocessing.imputation.Imputer(38)_copytrue
sklearn.preprocessing.imputation.Imputer(38)_missing_values"NaN"
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sklearn.preprocessing.imputation.Imputer(38)_verbose0
sklearn.preprocessing.data.StandardScaler(25)_copytrue
sklearn.preprocessing.data.StandardScaler(25)_with_meantrue
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sklearn.preprocessing._encoders.OneHotEncoder(9)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(9)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(9)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_learning_rate0.0017488341944454006
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_max_depth29
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_max_features0.5418147171348322
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_impurity_decrease0.3621076486086965
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_impurity_splitnull
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sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_samples_split19
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_weight_fraction_leaf0.27404385286640753
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sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_n_iter_no_change712
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_random_state17735
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_subsample0.3070888809147585
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_tol0.025181101074624356
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_validation_fraction0.008255659796921888
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_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.

17 Evaluation measures

Area under ROC curve
0.552 ± 0.019
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.3117 ± 0.0542
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1183 ± 0.0828
Cross-validation details (10-fold Crossvalidation)
KB relative information score
30.6057 ± 0.3097
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3718 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3748 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
846
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.2982 ± 0.049
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.3369 ± 0.0615
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.9991
Cross-validation details (10-fold Crossvalidation)
Recall
0.3369 ± 0.0615
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.992 ± 0.0024
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4329 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4302 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9937 ± 0.0024
Cross-validation details (10-fold Crossvalidation)