Run
32038

Run 32038

Task 115 (Supervised Classification) diabetes Uploaded 30-03-2021 by Continuous Integration
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Flow

sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler ,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimat or=sklearn.tree._classes.DecisionTreeClassifier))(5)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.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))(5)_memorynull
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))(5)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "boosting", "step_name": "boosting"}}]
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))(5)_verbosefalse
sklearn.preprocessing._data.StandardScaler(8)_copytrue
sklearn.preprocessing._data.StandardScaler(8)_with_meanfalse
sklearn.preprocessing._data.StandardScaler(8)_with_stdtrue
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(6)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(6)_learning_rate1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(6)_n_estimators50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(6)_random_state11474
sklearn.tree._classes.DecisionTreeClassifier(11)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(11)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(11)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(11)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(11)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(11)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(11)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(11)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(11)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(11)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(11)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(11)_random_state63886
sklearn.tree._classes.DecisionTreeClassifier(11)_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

0.6662 ± 0.0549
Per class
Cross-validation details (10-fold Crossvalidation)
0.694 ± 0.0516
Per class
Cross-validation details (10-fold Crossvalidation)
0.3295 ± 0.1108
Cross-validation details (10-fold Crossvalidation)
0.2962 ± 0.1216
Cross-validation details (10-fold Crossvalidation)
0.3073 ± 0.053
Cross-validation details (10-fold Crossvalidation)
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.6927 ± 0.053
Cross-validation details (10-fold Crossvalidation)
768
Per class
Cross-validation details (10-fold Crossvalidation)
0.6955 ± 0.0503
Per class
Cross-validation details (10-fold Crossvalidation)
0.6927 ± 0.053
Cross-validation details (10-fold Crossvalidation)
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.6761 ± 0.1167
Cross-validation details (10-fold Crossvalidation)
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.5543 ± 0.0481
Cross-validation details (10-fold Crossvalidation)
1.163 ± 0.1013
Cross-validation details (10-fold Crossvalidation)
0.6662 ± 0.0549
Cross-validation details (10-fold Crossvalidation)