Run
9245

Run 9245

Task 115 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler, boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator =sklearn.tree.tree.DecisionTreeClassifier))(8)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 to None.
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(8)_memorynull
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(8)_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.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.tree.DecisionTreeClassifier)(8)_algorithm"SAMME.R"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_learning_rate1.0
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_n_estimators50
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_random_state46849
sklearn.tree.tree.DecisionTreeClassifier(8)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(8)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(8)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(8)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(8)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(8)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(8)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(8)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(8)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(8)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(8)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(8)_random_state41810
sklearn.tree.tree.DecisionTreeClassifier(8)_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

0.6587 ± 0.0469
Per class
0.6883 ± 0.046
Per class
0.3158 ± 0.0973
0.2842 ± 0.1105
0.3125 ± 0.0481
0.4545 ± 0.0011
0.6875 ± 0.0481
768
Per class
0.6892 ± 0.0442
Per class
0.6875 ± 0.0481
0.9331 ± 0.0032
0.6876 ± 0.1061
0.4766 ± 0.0011
0.559 ± 0.0435
1.1728 ± 0.0917
0.6587 ± 0.0469