Run
154

Run 154

Task 119 (Supervised Classification) diabetes Uploaded 29-10-2019 by Continuous Integration
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TEST0690bb0a75sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.Simple Imputer,transformer=sklearn.compose._column_transformer.ColumnTransformer(n umeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocess ing._encoders.OneHotEncoder),classifier=sklearn.tree.tree.DecisionTreeClass ifier)(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``.
TEST0690bb0a75sklearn.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.tree.DecisionTreeClassifier)(1)_memorynull
TEST0690bb0a75sklearn.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.tree.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"}}]
TEST0690bb0a75sklearn.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.tree.DecisionTreeClassifier)(1)_verbosefalse
TEST0690bb0a75sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST0690bb0a75sklearn.impute._base.SimpleImputer(1)_copytrue
TEST0690bb0a75sklearn.impute._base.SimpleImputer(1)_fill_value-1
TEST0690bb0a75sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST0690bb0a75sklearn.impute._base.SimpleImputer(1)_strategy"constant"
TEST0690bb0a75sklearn.impute._base.SimpleImputer(1)_verbose0
TEST0690bb0a75sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
TEST0690bb0a75sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"passthrough"
TEST0690bb0a75sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
TEST0690bb0a75sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
TEST0690bb0a75sklearn.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]}}]
TEST0690bb0a75sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TEST0690bb0a75sklearn.preprocessing.data.StandardScaler(1)_copytrue
TEST0690bb0a75sklearn.preprocessing.data.StandardScaler(1)_with_meantrue
TEST0690bb0a75sklearn.preprocessing.data.StandardScaler(1)_with_stdtrue
TEST0690bb0a75sklearn.preprocessing._encoders.OneHotEncoder(1)_categorical_featuresnull
TEST0690bb0a75sklearn.preprocessing._encoders.OneHotEncoder(1)_categoriesnull
TEST0690bb0a75sklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TEST0690bb0a75sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST0690bb0a75sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TEST0690bb0a75sklearn.preprocessing._encoders.OneHotEncoder(1)_n_valuesnull
TEST0690bb0a75sklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_max_depthnull
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
TEST0690bb0a75sklearn.tree.tree.DecisionTreeClassifier(1)_random_state62501
TEST0690bb0a75sklearn.tree.tree.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)