Run
10417595

Run 10417595

Task 9952 (Supervised Classification) phoneme Uploaded 22-11-2019 by Jan van Rijn
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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,bernoullinb=sklearn.naive_bayes.BernoulliNB)(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.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))(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._base.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._base.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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_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))(2)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4]}}, {"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))(2)_verbosefalse
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(7)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(7)_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)(7)_verbosefalse
sklearn.preprocessing.imputation.Imputer(49)_axis0
sklearn.preprocessing.imputation.Imputer(49)_copytrue
sklearn.preprocessing.imputation.Imputer(49)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(49)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(49)_verbose0
sklearn.preprocessing.data.StandardScaler(35)_copytrue
sklearn.preprocessing.data.StandardScaler(35)_with_meantrue
sklearn.preprocessing.data.StandardScaler(35)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_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)(3)_verbosefalse
sklearn.impute._base.SimpleImputer(11)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(11)_copytrue
sklearn.impute._base.SimpleImputer(11)_fill_value-1
sklearn.impute._base.SimpleImputer(11)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(11)_strategy"constant"
sklearn.impute._base.SimpleImputer(11)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(16)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(16)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(16)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(27)_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)(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,bernoullinb=sklearn.naive_bayes.BernoulliNB)(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": "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)(1)_verbosefalse
sklearn.naive_bayes.BernoulliNB(8)_alpha1.0
sklearn.naive_bayes.BernoulliNB(8)_binarize0.0
sklearn.naive_bayes.BernoulliNB(8)_class_priornull
sklearn.naive_bayes.BernoulliNB(8)_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
0.8144 ± 0.02
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7511 ± 0.0146
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3866 ± 0.037
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2745 ± 0.0334
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2905 ± 0.0108
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
0.7482 ± 0.015
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7581 ± 0.0136
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8732 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Recall
0.7581 ± 0.0136
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7005 ± 0.026
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
0.401 ± 0.0126
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8805 ± 0.0276
Cross-validation details (10-fold Crossvalidation)