Run
10417142

Run 10417142

Task 146231 (Supervised Classification) tokyo1 Uploaded 15-10-2019 by Andreas Mueller
0 likes downloaded by 1 people 0 issues 0 downvotes , 2 total downloads
  • openml-python Sklearn_0.22.dev0.
Issue #Downvotes for this reason By


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_state46559
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": [21, 39]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cont", "step_name": "cont", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43]}}]
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

0.8957 ± 0.0385
Per class
Cross-validation details (10-fold Crossvalidation)
0.8878 ± 0.0299
Per class
Cross-validation details (10-fold Crossvalidation)
0.7577 ± 0.0656
Cross-validation details (10-fold Crossvalidation)
0.6331 ± 0.0534
Cross-validation details (10-fold Crossvalidation)
0.1828 ± 0.0223
Cross-validation details (10-fold Crossvalidation)
0.4613 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
959
Per class
Cross-validation details (10-fold Crossvalidation)
0.8885 ± 0.0295
Per class
Cross-validation details (10-fold Crossvalidation)
0.8874 ± 0.0293
Cross-validation details (10-fold Crossvalidation)
0.9433 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
0.8874 ± 0.0293
Per class
Cross-validation details (10-fold Crossvalidation)
0.3962 ± 0.0483
Cross-validation details (10-fold Crossvalidation)
0.4802 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
0.3124 ± 0.0365
Cross-validation details (10-fold Crossvalidation)
0.6504 ± 0.0761
Cross-validation details (10-fold Crossvalidation)