Run
6670

Run 6670

Task 119 (Supervised Classification) diabetes Uploaded 06-11-2019 by Continuous Integration
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Flow

TESTe842fe4d02sklearn.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``.
TESTe842fe4d02sklearn.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
TESTe842fe4d02sklearn.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": {"step_name": "imputer", "key": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"step_name": "transformer", "key": "transformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"step_name": "classifier", "key": "classifier"}}]
TESTe842fe4d02sklearn.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
TESTe842fe4d02sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTe842fe4d02sklearn.impute._base.SimpleImputer(1)_copytrue
TESTe842fe4d02sklearn.impute._base.SimpleImputer(1)_fill_value-1
TESTe842fe4d02sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTe842fe4d02sklearn.impute._base.SimpleImputer(1)_strategy"constant"
TESTe842fe4d02sklearn.impute._base.SimpleImputer(1)_verbose0
TESTe842fe4d02sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
TESTe842fe4d02sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"passthrough"
TESTe842fe4d02sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
TESTe842fe4d02sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
TESTe842fe4d02sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"argument_1": [], "step_name": "numeric", "key": "numeric"}}, {"oml-python:serialized_object": "component_reference", "value": {"argument_1": [0, 1, 2, 3, 4, 5, 6, 7], "step_name": "nominal", "key": "nominal"}}]
TESTe842fe4d02sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing.data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TESTe842fe4d02sklearn.preprocessing.data.StandardScaler(1)_copytrue
TESTe842fe4d02sklearn.preprocessing.data.StandardScaler(1)_with_meantrue
TESTe842fe4d02sklearn.preprocessing.data.StandardScaler(1)_with_stdtrue
TESTe842fe4d02sklearn.preprocessing._encoders.OneHotEncoder(1)_categorical_featuresnull
TESTe842fe4d02sklearn.preprocessing._encoders.OneHotEncoder(1)_categoriesnull
TESTe842fe4d02sklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TESTe842fe4d02sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TESTe842fe4d02sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TESTe842fe4d02sklearn.preprocessing._encoders.OneHotEncoder(1)_n_valuesnull
TESTe842fe4d02sklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_max_depthnull
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_splitnull
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
TESTe842fe4d02sklearn.tree.tree.DecisionTreeClassifier(1)_random_state62501
TESTe842fe4d02sklearn.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

0.5675
Per class
Cross-validation details (10% Holdout set)
0.6102
Per class
Cross-validation details (10% Holdout set)
0.1427
Cross-validation details (10% Holdout set)
0.1519
Cross-validation details (10% Holdout set)
0.3755
Cross-validation details (10% Holdout set)
0.4589
Cross-validation details (10% Holdout set)
0.6245
Cross-validation details (10% Holdout set)
253
Per class
Cross-validation details (10% Holdout set)
0.6062
Per class
Cross-validation details (10% Holdout set)
0.6245
Cross-validation details (10% Holdout set)
0.9463
Cross-validation details (10% Holdout set)
0.8182
Cross-validation details (10% Holdout set)
0.4813
Cross-validation details (10% Holdout set)
0.6128
Cross-validation details (10% Holdout set)
1.2733
Cross-validation details (10% Holdout set)
0.5675
Cross-validation details (10% Holdout set)