Run
10005036

Run 10005036

Task 9952 (Supervised Classification) phoneme Uploaded 17-01-2019 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_rate0.029083640714073745
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_max_depth6
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_max_features0.6809793126344699
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_impurity_decrease0.23544553066442886
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_impurity_splitnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_samples_leaf11
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_samples_split15
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_weight_fraction_leaf0.357169082548219
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_n_estimators252
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_n_iter_no_change828
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_random_state1703
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_subsample0.47316276710196903
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_tol0.015209563661853961
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_validation_fraction0.6584775111195875
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
0.8383 ± 0.0165
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7755 ± 0.0208
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4478 ± 0.0537
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1497.349 ± 11.8953
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3005 ± 0.0064
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
5404
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7733 ± 0.0216
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7811 ± 0.0189
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8733
Cross-validation details (10-fold Crossvalidation)
Recall
0.7811 ± 0.0189
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7245 ± 0.0153
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4554 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3817 ± 0.0092
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8382 ± 0.02
Cross-validation details (10-fold Crossvalidation)