Run
2

Run 2

Task 115 (Supervised Classification) diabetes Uploaded 29-10-2019 by Continuous Integration
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  • openml-python Sklearn_0.21.2. study_6 study_17 study_3170 study_3608 study_3994 study_3996 study_3999 study_4418 study_5311 study_8310 study_8624 study_8625 study_8626 study_9361 study_9363 study_9365 study_11766 study_11768 study_11770 study_11772 study_11774 study_11997 study_12006 study_13178 study_17523 study_17528
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Flow

sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler, boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator =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.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memorynull
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_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.tree.DecisionTreeClassifier))(1)_verbosefalse
sklearn.preprocessing.data.StandardScaler(1)_copytrue
sklearn.preprocessing.data.StandardScaler(1)_with_meanfalse
sklearn.preprocessing.data.StandardScaler(1)_with_stdtrue
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_algorithm"SAMME.R"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_learning_rate1.0
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_estimators50
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_random_state52113
sklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(1)_max_depthnull
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_state13416
sklearn.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

0.6692 ± 0.054
Per class
Cross-validation details (10-fold Crossvalidation)
0.6975 ± 0.0521
Per class
Cross-validation details (10-fold Crossvalidation)
0.3363 ± 0.1112
Cross-validation details (10-fold Crossvalidation)
0.3051 ± 0.1246
Cross-validation details (10-fold Crossvalidation)
0.3034 ± 0.0542
Cross-validation details (10-fold Crossvalidation)
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.6966 ± 0.0542
Cross-validation details (10-fold Crossvalidation)
768
Per class
Cross-validation details (10-fold Crossvalidation)
0.6985 ± 0.0511
Per class
Cross-validation details (10-fold Crossvalidation)
0.6966 ± 0.0542
Cross-validation details (10-fold Crossvalidation)
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.6675 ± 0.1195
Cross-validation details (10-fold Crossvalidation)
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.5508 ± 0.0499
Cross-validation details (10-fold Crossvalidation)
1.1556 ± 0.1054
Cross-validation details (10-fold Crossvalidation)
0.6692 ± 0.054
Cross-validation details (10-fold Crossvalidation)