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10418508

Run 10418508

Task 10093 (Supervised Classification) banknote-authentication Uploaded 22-11-2019 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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotE ncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.Var ianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestC lassifier)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.impute._base.SimpleImputer(10)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(10)_copytrue
sklearn.impute._base.SimpleImputer(10)_fill_value-1
sklearn.impute._base.SimpleImputer(10)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(10)_strategy"constant"
sklearn.impute._base.SimpleImputer(10)_verbose0
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._base.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._base.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._base.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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_transformer_weightsnull
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_verbosefalse
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(8)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(8)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(8)_verbosefalse
sklearn.preprocessing.imputation.Imputer(50)_axis0
sklearn.preprocessing.imputation.Imputer(50)_copytrue
sklearn.preprocessing.imputation.Imputer(50)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(50)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(50)_verbose0
sklearn.preprocessing.data.StandardScaler(36)_copytrue
sklearn.preprocessing.data.StandardScaler(36)_with_meantrue
sklearn.preprocessing.data.StandardScaler(36)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(17)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(17)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(17)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(17)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(17)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(17)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(17)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(28)_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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_memorynull
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbosefalse
sklearn.ensemble.forest.RandomForestClassifier(59)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(59)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(59)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(59)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(59)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(59)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(59)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(59)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(59)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(59)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(59)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(59)_n_estimators"warn"
sklearn.ensemble.forest.RandomForestClassifier(59)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(59)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(59)_random_state0
sklearn.ensemble.forest.RandomForestClassifier(59)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(59)_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
0.9996 ± 0.0004
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9891 ± 0.0062
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9779 ± 0.0125
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9544 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0257 ± 0.0081
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4939 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Number of instances
1372
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9891 ± 0.0059
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9891 ± 0.0062
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9911 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Recall
0.9891 ± 0.0062
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0521 ± 0.0164
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4969 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0917 ± 0.0223
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.1845 ± 0.0449
Cross-validation details (10-fold Crossvalidation)