Run
9877883

Run 9877883

Task 9967 (Supervised Classification) steel-plates-fault Uploaded 07-12-2018 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)(2)Automatically created scikit-learn flow.
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"most_frequent"
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.ensemble.gradient_boosting.GradientBoostingClassifier(14)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_learning_rate0.035114694220747936
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_max_depth5
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_max_features0.5248138025911044
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_impurity_decrease0.6970828697173914
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_impurity_splitnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_samples_leaf7
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_samples_split7
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_weight_fraction_leaf0.19584415937442323
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_n_estimators284
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_n_iter_no_change239
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_random_state5401
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_subsample0.41784182071402487
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_tol0.0011256748161048017
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_validation_fraction0.827651608703944
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_warm_startfalse
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))(1)_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))(1)_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))(1)_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))(1)_transformer_weightsnull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
sklearn.feature_selection.variance_threshold.VarianceThreshold(18)_threshold0.0
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)(2)_memorynull

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
0.8949 ± 0.0296
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8097 ± 0.0407
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.5751 ± 0.0909
Cross-validation details (10-fold Crossvalidation)
KB relative information score
737.2072 ± 8.284
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2916 ± 0.0158
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4531 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Number of instances
1941
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8095 ± 0.041
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8125 ± 0.04
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9313
Cross-validation details (10-fold Crossvalidation)
Recall
0.8125 ± 0.04
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6436 ± 0.0354
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4759 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3608 ± 0.0194
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.758 ± 0.0412
Cross-validation details (10-fold Crossvalidation)