Run
9916795

Run 9916795

Task 3512 (Supervised Classification) synthetic_control Uploaded 14-12-2018 by Scikit-learn Bot
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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)(1)Automatically created scikit-learn flow.
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)(1)_memorynull
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))(2)_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))(2)_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))(2)_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))(2)_transformer_weightsnull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_memorynull
sklearn.preprocessing.imputation.Imputer(31)_axis0
sklearn.preprocessing.imputation.Imputer(31)_copytrue
sklearn.preprocessing.imputation.Imputer(31)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(31)_strategy"most_frequent"
sklearn.preprocessing.imputation.Imputer(31)_verbose0
sklearn.preprocessing.data.StandardScaler(17)_copytrue
sklearn.preprocessing.data.StandardScaler(17)_with_meantrue
sklearn.preprocessing.data.StandardScaler(17)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_memorynull
sklearn.impute.SimpleImputer(2)_copytrue
sklearn.impute.SimpleImputer(2)_fill_value-1
sklearn.impute.SimpleImputer(2)_missing_valuesNaN
sklearn.impute.SimpleImputer(2)_strategy"constant"
sklearn.impute.SimpleImputer(2)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(5)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(5)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(5)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(5)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(5)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(5)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(19)_threshold0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_learning_rate0.029505119487819156
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_max_depth5
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_max_features0.319745502300012
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_impurity_decrease0.7623132463126469
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_impurity_splitnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_samples_leaf10
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_samples_split5
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_weight_fraction_leaf0.08043808023553539
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_n_estimators125
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_n_iter_no_change77
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_random_state10080
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_subsample0.22611547244549435
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_tol0.03205381237028801
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_validation_fraction0.304109952442416
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_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
0.9951 ± 0.0027
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9229 ± 0.044
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.908 ± 0.0509
Cross-validation details (10-fold Crossvalidation)
KB relative information score
447.2886 ± 1.4533
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1115 ± 0.0086
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2778
Cross-validation details (10-fold Crossvalidation)
Number of instances
600
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9258 ± 0.038
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9233 ± 0.0425
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.585
Cross-validation details (10-fold Crossvalidation)
Recall
0.9233 ± 0.0425
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4013 ± 0.0308
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3727
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1798 ± 0.0109
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4825 ± 0.0291
Cross-validation details (10-fold Crossvalidation)