Run
9728227

Run 9728227

Task 3889 (Supervised Classification) sylva_agnostic Uploaded 21-10-2018 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,svc=sklearn.svm.classes.SVC)(1)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.svm.classes.SVC(22)_C0.1420738158475554
sklearn.svm.classes.SVC(22)_cache_size200
sklearn.svm.classes.SVC(22)_class_weightnull
sklearn.svm.classes.SVC(22)_coef0-0.5956879145536977
sklearn.svm.classes.SVC(22)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(22)_degree5
sklearn.svm.classes.SVC(22)_gamma0.028904528099439747
sklearn.svm.classes.SVC(22)_kernel"poly"
sklearn.svm.classes.SVC(22)_max_iter-1
sklearn.svm.classes.SVC(22)_probabilityfalse
sklearn.svm.classes.SVC(22)_random_state17381
sklearn.svm.classes.SVC(22)_shrinkingfalse
sklearn.svm.classes.SVC(22)_tol0.050392489175342774
sklearn.svm.classes.SVC(22)_verbosefalse
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,svc=sklearn.svm.classes.SVC)(1)_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
0.896 ± 0.0194
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9852 ± 0.0029
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8664 ± 0.0278
Cross-validation details (10-fold Crossvalidation)
KB relative information score
11883.7363 ± 51.8612
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0141 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1156 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
14395
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9857 ± 0.0028
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9859 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3338
Cross-validation details (10-fold Crossvalidation)
Recall
0.9859 ± 0.0027
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.122 ± 0.0236
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2403 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1188 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4941 ± 0.0492
Cross-validation details (10-fold Crossvalidation)