Run
18026

Run 18026

Task 119 (Supervised Classification) diabetes Uploaded 12-11-2019 by Continuous Integration
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  • openml-python Sklearn_0.22.dev0.
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Flow

TEST3e51ba8b89sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.Simple Imputer,transformer=sklearn.compose._column_transformer.ColumnTransformer(n umeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preproces sing._encoders.OneHotEncoder),classifier=sklearn.tree._classes.DecisionTree Classifier)(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``.
TEST3e51ba8b89sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_memorynull
TEST3e51ba8b89sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "transformer", "step_name": "transformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
TEST3e51ba8b89sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbosefalse
TEST3e51ba8b89sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST3e51ba8b89sklearn.impute._base.SimpleImputer(1)_copytrue
TEST3e51ba8b89sklearn.impute._base.SimpleImputer(1)_fill_value-1
TEST3e51ba8b89sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST3e51ba8b89sklearn.impute._base.SimpleImputer(1)_strategy"constant"
TEST3e51ba8b89sklearn.impute._base.SimpleImputer(1)_verbose0
TEST3e51ba8b89sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
TEST3e51ba8b89sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"passthrough"
TEST3e51ba8b89sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
TEST3e51ba8b89sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
TEST3e51ba8b89sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_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, 6, 7]}}]
TEST3e51ba8b89sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TEST3e51ba8b89sklearn.preprocessing._data.StandardScaler(1)_copytrue
TEST3e51ba8b89sklearn.preprocessing._data.StandardScaler(1)_with_meantrue
TEST3e51ba8b89sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TEST3e51ba8b89sklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
TEST3e51ba8b89sklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TEST3e51ba8b89sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST3e51ba8b89sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TEST3e51ba8b89sklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_presort"deprecated"
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TEST3e51ba8b89sklearn.tree._classes.DecisionTreeClassifier(1)_splitter"best"

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.

18 Evaluation measures

0.5637
Per class
Cross-validation details (10% Holdout set)
0.6081
Per class
Cross-validation details (10% Holdout set)
0.1369
Cross-validation details (10% Holdout set)
0.1608
Cross-validation details (10% Holdout set)
0.3715
Cross-validation details (10% Holdout set)
0.4589
Cross-validation details (10% Holdout set)
0.6285
Cross-validation details (10% Holdout set)
253
Per class
Cross-validation details (10% Holdout set)
0.6057
Per class
Cross-validation details (10% Holdout set)
0.6285
Cross-validation details (10% Holdout set)
0.9463
Cross-validation details (10% Holdout set)
0.8096
Cross-validation details (10% Holdout set)
0.4813
Cross-validation details (10% Holdout set)
0.6095
Cross-validation details (10% Holdout set)
1.2666
Cross-validation details (10% Holdout set)
0.5637
Cross-validation details (10% Holdout set)