Run
9343131

Run 9343131

Task 3485 (Supervised Classification) scene Uploaded 10-10-2018 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)),adaboostclassifier=skle arn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree .tree.DecisionTreeClassifier))(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"mean"
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.tree.tree.DecisionTreeClassifier(29)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(29)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(29)_max_depth2
sklearn.tree.tree.DecisionTreeClassifier(29)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(29)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(29)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(29)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(29)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(29)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(29)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(29)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(29)_random_state35096
sklearn.tree.tree.DecisionTreeClassifier(29)_splitter"best"
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)),adaboostclassifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memorynull
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_algorithm"SAMME"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_learning_rate0.3443697990254402
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_n_estimators475
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_random_state5141

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
0.9891 ± 0.008
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9891 ± 0.0067
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9624 ± 0.0236
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-2524.302 ± 5.3428
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4282 ± 0.004
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2942 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Number of instances
2407
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9893 ± 0.0064
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9892 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.6786
Cross-validation details (10-fold Crossvalidation)
Recall
0.9892 ± 0.0065
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.4555 ± 0.0144
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3834 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4322 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1273 ± 0.0106
Cross-validation details (10-fold Crossvalidation)