Run
10418180

Run 10418180

Task 146821 (Supervised Classification) car Uploaded 22-11-2019 by Jan van Rijn
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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,bernoullinb=sklearn.naive_bayes.BernoulliNB)(2)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": []}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [0, 1, 2, 3, 4, 5]}}]
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,bernoullinb=sklearn.naive_bayes.BernoulliNB)(2)_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,bernoullinb=sklearn.naive_bayes.BernoulliNB)(2)_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": "bernoullinb", "step_name": "bernoullinb"}}]
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,bernoullinb=sklearn.naive_bayes.BernoulliNB)(2)_verbosefalse
sklearn.naive_bayes.BernoulliNB(9)_alpha1.0
sklearn.naive_bayes.BernoulliNB(9)_binarize0.0
sklearn.naive_bayes.BernoulliNB(9)_class_priornull
sklearn.naive_bayes.BernoulliNB(9)_fit_priortrue

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
0.9738 ± 0.0082
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8734 ± 0.0301
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7325 ± 0.0587
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6327 ± 0.0347
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1024 ± 0.0077
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.229 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Number of instances
1728
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8864 ± 0.0226
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8709 ± 0.0319
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.2058 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Recall
0.8709 ± 0.0319
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4472 ± 0.0344
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3381 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.213 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6299 ± 0.0352
Cross-validation details (10-fold Crossvalidation)