Run
10417107

Run 10417107

Task 219 (Supervised Classification) electricity Uploaded 15-10-2019 by Andreas Mueller
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  • openml-python Sklearn_0.22.dev0.
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=_ _main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders. OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute. _base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScale r)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(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.preprocessing.data.StandardScaler(31)_copytrue
sklearn.preprocessing.data.StandardScaler(31)_with_meantrue
sklearn.preprocessing.data.StandardScaler(31)_with_stdtrue
sklearn.preprocessing._encoders.OneHotEncoder(13)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(13)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(13)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(13)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(13)_sparsetrue
sklearn.tree.tree.DecisionTreeClassifier(51)_ccp_alpha0.0
sklearn.tree.tree.DecisionTreeClassifier(51)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(51)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(51)_max_depth1
sklearn.tree.tree.DecisionTreeClassifier(51)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(51)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(51)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(51)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(51)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(51)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(51)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(51)_presort"deprecated"
sklearn.tree.tree.DecisionTreeClassifier(51)_random_state26316
sklearn.tree.tree.DecisionTreeClassifier(51)_splitter"best"
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_verbosefalse
sklearn.impute._base.SimpleImputer(5)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(5)_copytrue
sklearn.impute._base.SimpleImputer(5)_fill_valuenull
sklearn.impute._base.SimpleImputer(5)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(5)_strategy"median"
sklearn.impute._base.SimpleImputer(5)_verbose0
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [1]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cont", "step_name": "cont", "argument_1": [0, 2, 3, 4, 5, 6, 7]}}]
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_verbosefalse
sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "anothersimpleimputer", "step_name": "anothersimpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
__main__.AnotherSimpleImputer(1)_add_indicatorfalse
__main__.AnotherSimpleImputer(1)_copytrue
__main__.AnotherSimpleImputer(1)_fill_valuenull
__main__.AnotherSimpleImputer(1)_missing_valuesNaN
__main__.AnotherSimpleImputer(1)_strategy"most_frequent"
__main__.AnotherSimpleImputer(1)_verbose0

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

0.7283 ± 0.0083
Per class
Cross-validation details (10-fold Crossvalidation)
0.7486 ± 0.0082
Per class
Cross-validation details (10-fold Crossvalidation)
0.484 ± 0.0167
Cross-validation details (10-fold Crossvalidation)
0.2912 ± 0.009
Cross-validation details (10-fold Crossvalidation)
0.3659 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
0.4886 ± 0
Cross-validation details (10-fold Crossvalidation)
45312
Per class
Cross-validation details (10-fold Crossvalidation)
0.7651 ± 0.0077
Per class
Cross-validation details (10-fold Crossvalidation)
0.7574 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
0.9835 ± 0
Cross-validation details (10-fold Crossvalidation)
0.7574 ± 0.0076
Per class
Cross-validation details (10-fold Crossvalidation)
0.7488 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
0.4943 ± 0
Cross-validation details (10-fold Crossvalidation)
0.4277 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
0.8653 ± 0.0094
Cross-validation details (10-fold Crossvalidation)