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Run 2274

Task 115 (Supervised Classification) diabetes Uploaded 17-10-2024 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))(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._classes.DecisionTreeClassifier))(1)_memorynull
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.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._classes.DecisionTreeClassifier))(1)_verbosefalse
sklearn.preprocessing._data.StandardScaler(2)_copytrue
sklearn.preprocessing._data.StandardScaler(2)_with_meanfalse
sklearn.preprocessing._data.StandardScaler(2)_with_stdtrue
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_learning_rate1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_estimators50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state15808
sklearn.tree._classes.DecisionTreeClassifier(2)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(2)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(2)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(2)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(2)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(2)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(2)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(2)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(2)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(2)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(2)_random_state32822
sklearn.tree._classes.DecisionTreeClassifier(2)_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

0.6734 ± 0.0544
Per class
0.704 ± 0.0539
Per class
0.3478 ± 0.1142
0.323 ± 0.1294
0.2956 ± 0.0561
0.4545 ± 0.0011
0.7044 ± 0.0561
768
Per class
0.7037 ± 0.0517
Per class
0.7044 ± 0.0561
0.9331 ± 0.0032
0.6503 ± 0.1241
0.4766 ± 0.0011
0.5437 ± 0.0531
1.1406 ± 0.1124
0.6734 ± 0.0544