OpenML
9869235

Run 9869235

Task 58 (Supervised Classification) waveform-5000 Uploaded 07-12-2018 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"median"
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_rate0.5087572173931088
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_max_depth9
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_max_features0.681970542888373
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_impurity_decrease0.06667268863007181
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_impurity_splitnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_samples_leaf9
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_samples_split4
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_min_weight_fraction_leaf0.27555095236334015
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_n_estimators66
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_n_iter_no_change608
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_random_state36365
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_subsample0.7406290583782114
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_tol0.03850209113919007
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(15)_validation_fraction0.6194672815358406
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
0.9599 ± 0.0055
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8331 ± 0.0109
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7498 ± 0.0162
Cross-validation details (10-fold Crossvalidation)
KB relative information score
3694.8564 ± 6.2161
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1358 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4444 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
5000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8333 ± 0.011
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8332 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.5849
Cross-validation details (10-fold Crossvalidation)
Recall
0.8332 ± 0.0108
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3055 ± 0.0146
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2772 ± 0.0096
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5881 ± 0.0204
Cross-validation details (10-fold Crossvalidation)