Run
10417081

Run 10417081

Task 146802 (Supervised Classification) collins 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(cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn .impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.Stand ardScaler)),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.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_state44904
sklearn.tree.tree.DecisionTreeClassifier(51)_splitter"best"
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(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(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(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(cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_transformers[{"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]}}]
sklearn.compose._column_transformer.ColumnTransformer(cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_verbosefalse
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

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.

15 Evaluation measures

0.6331 ± 0.0131
Per class
Cross-validation details (10-fold Crossvalidation)
0.0428 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
0.1416 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
0.062 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.0636 ± 0
Cross-validation details (10-fold Crossvalidation)
1000
Per class
Cross-validation details (10-fold Crossvalidation)
0.107 ± 0.0048
Cross-validation details (10-fold Crossvalidation)
4.6489 ± 0.0159
Cross-validation details (10-fold Crossvalidation)
0.107 ± 0.0048
Per class
Cross-validation details (10-fold Crossvalidation)
0.9743 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
0.1783 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.1761 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.9876 ± 0.0019
Cross-validation details (10-fold Crossvalidation)