Run
146

Run 146

Task 119 (Supervised Classification) diabetes Uploaded 29-10-2019 by Continuous Integration
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classi fier=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.tree.tree.DecisionTreeClassifier(1)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(1)_max_depth1
sklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(1)_random_state48316
sklearn.tree.tree.DecisionTreeClassifier(1)_splitter"best"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,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": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbosefalse
sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(1)_copytrue
sklearn.impute._base.SimpleImputer(1)_fill_valuenull
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"mean"
sklearn.impute._base.SimpleImputer(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.

18 Evaluation measures

0.6933
Per class
Cross-validation details (10% Holdout set)
0.7004
Per class
Cross-validation details (10% Holdout set)
0.3703
Cross-validation details (10% Holdout set)
0.1723
Cross-validation details (10% Holdout set)
0.3842
Cross-validation details (10% Holdout set)
0.4589
Cross-validation details (10% Holdout set)
0.6957
Cross-validation details (10% Holdout set)
253
Per class
Cross-validation details (10% Holdout set)
0.7128
Per class
Cross-validation details (10% Holdout set)
0.6957
Cross-validation details (10% Holdout set)
0.9463
Cross-validation details (10% Holdout set)
0.8372
Cross-validation details (10% Holdout set)
0.4813
Cross-validation details (10% Holdout set)
0.4471
Cross-validation details (10% Holdout set)
0.9291
Cross-validation details (10% Holdout set)
0.6933
Cross-validation details (10% Holdout set)