Run
9997228

Run 9997228

Task 9914 (Supervised Classification) wilt Uploaded 16-01-2019 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)(3)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)(3)_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))(3)_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))(3)_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))(3)_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))(3)_transformer_weightsnull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(3)_memorynull
sklearn.preprocessing.imputation.Imputer(34)_axis0
sklearn.preprocessing.imputation.Imputer(34)_copytrue
sklearn.preprocessing.imputation.Imputer(34)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(34)_strategy"most_frequent"
sklearn.preprocessing.imputation.Imputer(34)_verbose0
sklearn.preprocessing.data.StandardScaler(20)_copytrue
sklearn.preprocessing.data.StandardScaler(20)_with_meantrue
sklearn.preprocessing.data.StandardScaler(20)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_memorynull
sklearn.impute.SimpleImputer(6)_copytrue
sklearn.impute.SimpleImputer(6)_fill_value-1
sklearn.impute.SimpleImputer(6)_missing_valuesNaN
sklearn.impute.SimpleImputer(6)_strategy"constant"
sklearn.impute.SimpleImputer(6)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(6)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(6)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(6)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(6)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(6)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(6)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(21)_threshold0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_learning_rate0.6065710528528977
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_max_depth1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_max_features0.515464692024766
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_impurity_decrease0.40200072301681744
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_impurity_splitnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_samples_leaf12
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_samples_split19
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_weight_fraction_leaf0.3206624555193603
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_n_estimators338
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_n_iter_no_change98
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_random_state33128
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_subsample0.42120258716237846
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_tol0.009933901198955545
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_validation_fraction0.1657128828304527
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_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
0.947 ± 0.0133
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9656 ± 0.0047
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6277 ± 0.0594
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1908.2811 ± 25.1704
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0532 ± 0.002
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Number of instances
4839
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9671 ± 0.0044
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9694 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3037
Cross-validation details (10-fold Crossvalidation)
Recall
0.9694 ± 0.0035
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.5204 ± 0.0202
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.155 ± 0.0068
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6862 ± 0.0313
Cross-validation details (10-fold Crossvalidation)