Run
9547965

Run 9547965

Task 9977 (Supervised Classification) nomao Uploaded 11-10-2018 by Jan van Rijn
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=s klearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imp uter,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=skle arn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotenco der=sklearn.preprocessing._encoders.OneHotEncoder)),extratreesclassifier=sk learn.ensemble.forest.ExtraTreesClassifier)(1)Automatically created scikit-learn flow.
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformer_weightsnull
sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
sklearn.impute.MissingIndicator(1)_error_on_newfalse
sklearn.impute.MissingIndicator(1)_features"missing-only"
sklearn.impute.MissingIndicator(1)_missing_valuesNaN
sklearn.impute.MissingIndicator(1)_sparse"auto"
sklearn.preprocessing.imputation.Imputer(29)_axis0
sklearn.preprocessing.imputation.Imputer(29)_copytrue
sklearn.preprocessing.imputation.Imputer(29)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(29)_strategy"median"
sklearn.preprocessing.imputation.Imputer(29)_verbose0
sklearn.preprocessing.data.StandardScaler(14)_copytrue
sklearn.preprocessing.data.StandardScaler(14)_with_meantrue
sklearn.preprocessing.data.StandardScaler(14)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
sklearn.impute.SimpleImputer(1)_copytrue
sklearn.impute.SimpleImputer(1)_fill_value-1
sklearn.impute.SimpleImputer(1)_missing_valuesNaN
sklearn.impute.SimpleImputer(1)_strategy"constant"
sklearn.impute.SimpleImputer(1)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(3)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(3)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(3)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_memorynull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_bootstrapfalse
sklearn.ensemble.forest.ExtraTreesClassifier(10)_class_weightnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_criterion"gini"
sklearn.ensemble.forest.ExtraTreesClassifier(10)_max_depthnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_max_features0.9829155064344877
sklearn.ensemble.forest.ExtraTreesClassifier(10)_max_leaf_nodesnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_impurity_decrease0.0
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_impurity_splitnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_samples_leaf19
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_samples_split2
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.ExtraTreesClassifier(10)_n_estimators100
sklearn.ensemble.forest.ExtraTreesClassifier(10)_n_jobsnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_oob_scorefalse
sklearn.ensemble.forest.ExtraTreesClassifier(10)_random_state51160
sklearn.ensemble.forest.ExtraTreesClassifier(10)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(10)_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
0.9772 ± 0.0031
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9311 ± 0.006
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8314 ± 0.0149
Cross-validation details (10-fold Crossvalidation)
KB relative information score
26190.1041 ± 41.7837
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0985 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4081 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Number of instances
34465
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9312 ± 0.0061
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9309 ± 0.006
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.863
Cross-validation details (10-fold Crossvalidation)
Recall
0.9309 ± 0.006
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2413 ± 0.0104
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4517 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2252 ± 0.0081
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4987 ± 0.018
Cross-validation details (10-fold Crossvalidation)